Journal articles on the topic 'Clusters detection'
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1
AHMAD, WASEEM, and AJIT NARAYANAN. "OUTLIER DETECTION USING HUMORAL-MEDIATED CLUSTERING (HAIS)." International Journal of Computational Intelligence and Applications 11, no. 01 (March 2012): 1250003. http://dx.doi.org/10.1142/s1469026812500034.
Abstract:
Outlier detection has important applications in various data mining domains such as fraud detection, intrusion detection, customers' behavior and employees' performance analysis. Outliers are characterized by being significantly or "interestingly" different from the rest of the data. In this paper, a novel cluster-based outlier detection method is proposed using a humoral-mediated clustering algorithm (HAIS) based on concepts of antibody secretion in natural immune systems. The proposed method finds meaningful clusters as well as outliers simultaneously. This is an iterative approach where only clusters above threshold (larger sized clusters) are carried forward to the next cycle of cluster formation while removing small sized clusters. This paper also demonstrates through experimental results that the mere existence of outliers severely affects the clustering outcome, and removing those outliers can result in better clustering solutions. The feasibility of the method is demonstrated through simulated datasets, current datasets from the literature as well as a real-world doctors' performance evaluation dataset where the task is to identify potentially under-performing doctors. The results indicate that HAIS has capabilities of detecting single point as well as cluster-based outliers.
2
Xu, Weiwei, Miriam E. Ramos-Ceja, Florian Pacaud, Thomas H. Reiprich, and Thomas Erben. "Catalog of X-ray-selected extended galaxy clusters from the ROSAT All-Sky Survey (RXGCC)." Astronomy & Astrophysics 658 (February 2022): A59. http://dx.doi.org/10.1051/0004-6361/202140908.
Abstract:
Context. There is a known tension between cosmological parameter constraints obtained from the primary cosmic microwave background and those drawn from galaxy cluster samples. One possible explanation for this discrepancy may be that the incomplete character of detected clusters is higher than estimated and, as a result, certain types of groups or galaxy clusters have been overlooked in the past. Aims. We aim to search for galaxy groups and clusters with particularly extended surface brightness distributions by creating a new X-ray-selected catalog of extended galaxy clusters from the ROSAT All-Sky Survey (RASS), based on a dedicated source detection and characterization algorithm that is optimized for extended sources. Methods. Our state-of-the-art algorithm includes multi-resolution filtering, source detection, and characterization. On the basis of extensive simulations, we investigated the detection efficiency and sample purity. We used previous cluster catalogs in X-ray and other bands, as well as spectroscopic and photometric redshifts of galaxies to identify clusters. Results. We report a catalog of galaxy clusters at high galactic latitude based on the ROSAT All-sky Survey, known as the RASS-based extended X-ray Galaxy Cluster Catalog, which includes 944 groups and clusters. Of this number, 641 clusters have been previously identified based on intra-cluster medium (ICM) emission (Bronze), 154 known optical and infrared clusters are detected as X-ray clusters for the first time (Silver) and 149 are identified as clusters for the first time (Gold). Based on 200 simulations, the contamination ratio of the detections that were identified as clusters by ICM emission and the detections that were identified as optical and infrared clusters in previous work is 0.008 and 0.100, respectively. Compared with the Bronze sample, the Gold+Silver sample is less luminous, less massive, and exhibits a flatter surface brightness profile. Specifically, the median flux in [0.1−2.4] keV band for Gold+Silver and Bronze sample is 2.496 × 10−12 erg s−1 cm−2 and 4.955 × 10−12 erg s−1 cm−2, respectively. The median value of β (the slope of cluster surface brightness profile) is 0.76 and 0.83 for the Gold+Silver and Bronze sample, respectively.
3
Reduzzi, Carolina, Serena Di Cosimo, Lorenzo Gerratana, Rosita Motta, Antonia Martinetti, Andrea Vingiani, Paolo D’Amico, et al. "Circulating Tumor Cell Clusters Are Frequently Detected in Women with Early-Stage Breast Cancer." Cancers 13, no. 10 (May 13, 2021): 2356. http://dx.doi.org/10.3390/cancers13102356.
Abstract:
The clinical relevance of circulating tumor cell clusters (CTC-clusters) in breast cancer (BC) has been mostly studied using the CellSearch®, a marker-dependent method detecting only epithelial-enriched clusters. However, due to epithelial-to-mesenchymal transition, resorting to marker-independent approaches can improve CTC-cluster detection. Blood samples collected from healthy donors and spiked-in with tumor mammospheres, or from BC patients, were processed for CTC-cluster detection with 3 technologies: CellSearch®, CellSieve™ filters, and ScreenCell® filters. In spiked-in samples, the 3 technologies showed similar recovery capability, whereas, in 19 clinical samples processed in parallel with CellSearch® and CellSieve™ filters, filtration allowed us to detect more CTC-clusters than CellSearch® (median number = 7 versus 1, p = 0.0038). Next, samples from 37 early BC (EBC) and 23 metastatic BC (MBC) patients were processed using ScreenCell® filters for attaining both unbiased enrichment and marker-independent identification (based on cytomorphological criteria). At baseline, CTC-clusters were detected in 70% of EBC cases and in 20% of MBC patients (median number = 2, range 0–20, versus 0, range 0–15, p = 0.0015). Marker-independent approaches for CTC-cluster assessment improve detection and show that CTC-clusters are more frequent in EBC than in MBC patients, a novel finding suggesting that dissemination of CTC-clusters is an early event in BC natural history.
4
Mas, J.-F., A. Pérez Vega, and A. Ghilardi. "EFFECT OF THE DELAY IN THE REPORTS OF COVID-19 CASES ON NEAR REAL-TIME CLUSTERS DETECTION." International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLVIII-1/W2-2023 (December 13, 2023): 457–62. http://dx.doi.org/10.5194/isprs-archives-xlviii-1-w2-2023-457-2023.
Abstract:
Abstract. The COVID-19 pandemic has strongly impacted the vast majority of countries in the world. As of today (April 12th, 2023), more than 762 million confirmed cases and nearly 6.9 million deaths are considered widely underestimated. During a pandemic, detecting clusters of patients is crucial to allocate resources and aiding decision-making better as emergent outbreaks continue to grow. However, delays in reporting suspected or confirmed cases can affect the detection of clusters in near real-time. This study aimed to assess whether the delays in reporting COVID-19 in Mexico presented specific Spatiotemporal patterns and whether they significantly affected the detection of clusters. To do this, we used the daily records of the Mexican Ministry of Health for three dates at the beginning and during the increase in cases of the fourth wave (January 2022). We compared the clusters obtained using the data available on the same date and during the following days, including delayed data. We carried out cluster detection using the flexible spatial scan statistic (FlexScan) on the R platform. The results indicate that the spatial distribution of delays was heterogeneous and that delays affect cluster detection.
5
Baker, Meghan A., Deborah S. Yokoe, John Stelling, Ken Kleinman, Rebecca E. Kaganov, Alyssa R. Letourneau, Neha Varma, et al. "Automated outbreak detection of hospital-associated pathogens: Value to infection prevention programs." Infection Control & Hospital Epidemiology 41, no. 9 (June 10, 2020): 1016–21. http://dx.doi.org/10.1017/ice.2020.233.
Abstract:
AbstractObjective:To assess the utility of an automated, statistically-based outbreak detection system to identify clusters of hospital-acquired microorganisms.Design:Multicenter retrospective cohort study.Setting:The study included 43 hospitals using a common infection prevention surveillance system.Methods:A space–time permutation scan statistic was applied to hospital microbiology, admission, discharge, and transfer data to identify clustering of microorganisms within hospital locations and services. Infection preventionists were asked to rate the importance of each cluster. A convenience sample of 10 hospitals also provided information about clusters previously identified through their usual surveillance methods.Results:We identified 230 clusters in 43 hospitals involving Gram-positive and -negative bacteria and fungi. Half of the clusters progressed after initial detection, suggesting that early detection could trigger interventions to curtail further spread. Infection preventionists reported that they would have wanted to be alerted about 81% of these clusters. Factors associated with clusters judged to be moderately or highly concerning included high statistical significance, large size, and clusters involving Clostridioides difficile or multidrug-resistant organisms. Based on comparison data provided by the convenience sample of hospitals, only 9 (18%) of 51 clusters detected by usual surveillance met statistical significance, and of the 70 clusters not previously detected, 58 (83%) involved organisms not routinely targeted by the hospitals’ surveillance programs. All infection prevention programs felt that an automated outbreak detection tool would improve their ability to detect outbreaks and streamline their work.Conclusions:Automated, statistically-based outbreak detection can increase the consistency, scope, and comprehensiveness of detecting hospital-associated transmission.
6
Too, L. S., J. Pirkis, A. Milner, and M. J. Spittal. "Clusters of suicides and suicide attempts: detection, proximity and correlates." Epidemiology and Psychiatric Sciences 26, no. 5 (June 9, 2016): 491–500. http://dx.doi.org/10.1017/s2045796016000391.
Abstract:
Background.A suicide cluster is defined as a higher number of observed cases occurring in space and/or time than would typically be expected. Previous research has largely focused on identifying clusters of suicides, while there has been comparatively limited research on clusters of suicide attempts. We sought to identify clusters of both types of behaviour, and having done that, identify the factors that distinguish suicide attempts inside a cluster from those that were outside a cluster.Methods.We used data from Western Australia from 2000 to 2011. We defined suicide attempts as admissions to hospital for deliberate self-harm and suicides as deaths due to deliberate self-harm. Using an analytic strategy that accounted for the repetition of attempted suicide within a cluster, we performed spatial-temporal analysis using Poisson discrete scan statistics to detect clusters of suicide attempts and clusters of suicides. Logistic regression was then used to compare clustered attempts with non-clustered attempts to identify risk factors for an attempt being in a cluster.Results.We detected 350 (1%) suicide attempts occurring within seven spatial-temporal clusters and 12 (0.6%) suicides occurring within two spatial-temporal clusters. Both of the suicide clusters were located within a larger but later suicide attempt cluster. In multivariate analysis, suicide attempts by individuals who lived in areas of low socioeconomic status had higher odds of being in a cluster than those living in areas of high socioeconomic status [odds ratio (OR) = 29.1, 95% confidence interval (CI) = 6.3–135.5]. A one percentage-point increase in the proportion of people who had changed address in the last year was associated with a 60% increase in the odds of the attempt being within a cluster (OR = 1.60, 95% CI = 1.29–1.98) and a one percentage-point increase in the proportion of Indigenous people in the area was associated with a 7% increase in the suicide being within a cluster (OR = 1.07, 95% CI = 1.00–1.13). Age, sex, marital status, employment status, method of harm, remoteness, percentage of people in rented accommodation and percentage of unmarried people were not associated with the odds of being in a suicide attempt cluster.Conclusions.Early identification of and responding to suicide clusters may reduce the likelihood of subsequent clusters forming. The mechanisms, however, that underlie clusters forming is poorly understood.
7
Barthakur, Pijush, Manoj Dahal, and Mrinal Kanti Ghose. "CluSiBotHealer: Botnet Detection through Similarity Analysis of Clusters." Journal of Advances in Computer Networks 3, no. 1 (2015): 49–55. http://dx.doi.org/10.7763/jacn.2015.v3.141.
8
Kremer, Kyle, Dongzi Li, Wenbin Lu, Anthony L. Piro, and Bing Zhang. "Prospects for Detecting Fast Radio Bursts in the Globular Clusters of Nearby Galaxies." Astrophysical Journal 944, no. 1 (February 1, 2023): 6. http://dx.doi.org/10.3847/1538-4357/acabbf.
Abstract:
Abstract The recent detection of a repeating fast radio burst (FRB) in an old globular cluster in M81 challenges traditional FRB formation mechanisms based on the magnetic activity of young neutron stars formed in core-collapse supernovae. Furthermore, the detection of this repeater in such a nearby galaxy implies a high local universe rate of similar events in globular clusters. Building off the properties inferred from the M81 FRB, we predict the number of FRB sources in nearby (d ≲ 20 Mpc) galaxies with large globular cluster systems known. Incorporating the uncertain burst energy distribution, we estimate the rate of bursts detectable in these galaxies by radio instruments such as FAST and MeerKat. Of all local galaxies, we find M87 is the best candidate for FRB detections. We predict that M87's globular cluster system contains ( 10 ) FRB sources at present and that a dedicated radio survey (by either FAST or MeerKat) of ( 10 ) hr has a 90% probability of detecting a globular cluster FRB in M87. The detection of even a handful of additional globular cluster FRBs would provide invaluable constraints on FRB mechanisms and population properties. Previous studies have demonstrated young neutron stars formed following the collapse of dynamically formed massive white dwarf binary mergers may provide the most natural mechanism for these bursts. We explore the white dwarf merger scenario using a suite of N-body cluster models, focusing in particular on such mergers in M87's clusters. We describe a number of outstanding features of this scenario that in principle may be testable with an ensemble of observed FRBs in nearby globular clusters.
9
Mirmelstein, M., M. Shimon, and Y. Rephaeli. "Detection likelihood of cluster-induced CMB polarization." Astronomy & Astrophysics 644 (November 30, 2020): A36. http://dx.doi.org/10.1051/0004-6361/201834657.
Abstract:
Nearby galaxy clusters can potentially induce sub-microkelvin polarization signals in the cosmic microwave background (CMB) at characteristic scales of a few arcminutes. We explore four such polarization signals induced in a rich nearby fiducial cluster and calculate the likelihood of their detection by a telescope project with capabilities such as those of the Simons Observatory (SO). In our feasibility analysis, we include instrumental noise, primordial CMB anisotropy, statistical thermal Sunyaev-Zeldovich (SZ) cluster signal, and point source confusion, assuming a few percent of the nominal telescope observation time of an SO-like project. Our analysis indicates that the thermal SZ intensity can be sensitively mapped in rich nearby clusters and that the kinematic SZ intensity can be measured with high statistical significance toward a fast moving nearby cluster. The detection of polarized SZ signals will be quite challenging but could still be feasible toward several very rich nearby clusters with very high SZ intensity. The polarized SZ signal from a sample of ∼20 clusters can be statistically detected at S/N ∼ 3, if observed for several months.
10
Masuda, Ryo, and Ryo Inoue. "Point Event Cluster Detection via the Bayesian Generalized Fused Lasso." ISPRS International Journal of Geo-Information 11, no. 3 (March 11, 2022): 187. http://dx.doi.org/10.3390/ijgi11030187.
Abstract:
Spatial cluster detection is one of the focus areas of spatial analysis, whose objective is the identification of clusters from spatial distributions of point events aggregated in districts with small areas. Choi et al. (2018) formulated cluster detection as a parameter estimation problem to leverage the parameter selection capability of the sparse modeling method called the generalized fused lasso. Although this work is superior to conventional methods for detecting multiple clusters, its estimation results are limited to point estimates. This study therefore extended the above work as a Bayesian cluster detection method to describe the probabilistic variations of clustering results. The proposed method combines multiple sparsity-inducing priors and encourages sparse solutions induced by the generalized fused lasso. Evaluations were performed with simulated and real-world distributions of point events to demonstrate that the proposed method provides new information on the quantified reliabilities of clustering results at the district level while achieving comparable detection performances to that of the previous work.
11
FOGGIA, PASQUALE, GENNARO PERCANNELLA, CARLO SANSONE, and MARIO VENTO. "A GRAPH-BASED ALGORITHM FOR CLUSTER DETECTION." International Journal of Pattern Recognition and Artificial Intelligence 22, no. 05 (August 2008): 843–60. http://dx.doi.org/10.1142/s0218001408006557.
Abstract:
In some Computer Vision applications there is the need for grouping, in one or more clusters, only a part of the whole dataset. This happens, for example, when samples of interest for the application at hand are present together with several noisy samples. In this paper we present a graph-based algorithm for cluster detection that is particularly suited for detecting clusters of any size and shape, without the need of specifying either the actual number of clusters or the other parameters. The algorithm has been tested on data coming from two different computer vision applications. A comparison with other four state-of-the-art graph-based algorithms was also provided, demonstrating the effectiveness of the proposed approach.
12
Tarrío, P., J. B. Melin, and M. Arnaud. "A matched filter approach for blind joint detection of galaxy clusters in X-ray and SZ surveys." Astronomy & Astrophysics 614 (June 2018): A82. http://dx.doi.org/10.1051/0004-6361/201731984.
Abstract:
The combination of X-ray and Sunyaev–Zeldovich (SZ) observations can potentially improve the cluster detection efficiency, when compared to using only one of these probes, since both probe the same medium, the hot ionized gas of the intra-cluster medium. We present a method based on matched multifrequency filters (MMF) for detecting galaxy clusters from SZ and X-ray surveys. This method builds on a previously proposed joint X-ray–SZ extraction method and allows the blind detection of clusters, that is finding new clusters without knowing their position, size, or redshift, by searching on SZ and X-ray maps simultaneously. The proposed method is tested using data from the ROSAT all-sky survey and from the Planck survey. The evaluation is done by comparison with existing cluster catalogues in the area of the sky covered by the deep SPT survey. Thanks to the addition of the X-ray information, the joint detection method is able to achieve simultaneously better purity, better detection efficiency, and better position accuracy than its predecessor Planck MMF, which is based on SZ maps alone. For a purity of 85%, the X-ray–SZ method detects 141 confirmed clusters in the SPT region; to detect the same number of confirmed clusters with Planck MMF, we would need to decrease its purity to 70%. We provide a catalogue of 225 sources selected by the proposed method in the SPT footprint, with masses ranging between 0.7 and 14.5 ×1014 M⊙ and redshifts between 0.01 and 1.2.
13
Ullah, Sami, Fahim Raees, and Zahid Khan. "Detection of Space-Time Disease Clusters Using A Matrix Factorization Method." STATISTICS, COMPUTING AND INTERDISCIPLINARY RESEARCH 4, no. 1 (April 26, 2022): 1–9. http://dx.doi.org/10.52700/scir.v4i1.78.
Abstract:
Space-time cluster detection has important applications in public health management and epidemiology to devise disease prevention strategies and to find the causes of a particular disease outbreak in a country. This study introduced a new method to detect the potential space-time clusters with no restriction on cluster shape and size and further visualize them distinctly on the heat map. The proposed algorithm is based on matrix factorization technique to find the significant components in spatial as well as temporal dimension. Applications to malaria data in Khyber Pakhtunkhwa, Pakistan shows that the proposed method is effective in detecting the potential clusters.
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Ullah, Sami, Fahim Raees, and Zahid Khan. "Detection of Space-Time Disease Clusters Using A Matrix Factorization Method." STATISTICS, COMPUTING AND INTERDISCIPLINARY RESEARCH 4, no. 1 (April 26, 2022): 1–9. http://dx.doi.org/10.52700/scir.v4i1.78.
Abstract:
Space-time cluster detection has important applications in public health management and epidemiology to devise disease prevention strategies and to find the causes of a particular disease outbreak in a country. This study introduced a new method to detect the potential space-time clusters with no restriction on cluster shape and size and further visualize them distinctly on the heat map. The proposed algorithm is based on matrix factorization technique to find the significant components in spatial as well as temporal dimension. Applications to malaria data in Khyber Pakhtunkhwa, Pakistan shows that the proposed method is effective in detecting the potential clusters.
15
Ullah, Sami, Fahim Raees, and Zahid Khan. "Detection of Space-Time Disease Clusters Using A Matrix Factorization Method." STATISTICS, COMPUTING AND INTERDISCIPLINARY RESEARCH 4, no. 1 (April 26, 2022): 1–9. http://dx.doi.org/10.52700/scir.v4i1.78.
Abstract:
Space-time cluster detection has important applications in public health management and epidemiology to devise disease prevention strategies and to find the causes of a particular disease outbreak in a country. This study introduced a new method to detect the potential space-time clusters with no restriction on cluster shape and size and further visualize them distinctly on the heat map. The proposed algorithm is based on matrix factorization technique to find the significant components in spatial as well as temporal dimension. Applications to malaria data in Khyber Pakhtunkhwa, Pakistan shows that the proposed method is effective in detecting the potential clusters.
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ROUNDS, J. M., D. J. BOXRUD, S. L. JAWAHIR, and K. E. SMITH. "Dynamics ofEscherichia coliO157:H7 outbreak detection and investigation, Minnesota 2000–2008." Epidemiology and Infection 140, no. 8 (November 18, 2011): 1430–38. http://dx.doi.org/10.1017/s0950268811002330.
Abstract:
SUMMARYWe determined characteristics ofEscherichia coliO157:H7 pulsed-field gel electrophoresis clusters that predict their being solved (i.e. that result in identification of a confirmed outbreak). Clusters were investigated by the Minnesota Department of Health (MDH) using a dynamic iterative model. During 2000–2008, 19 (23%) of 84 clusters were solved. Clusters of ⩾3 isolates were more likely to be solved than clusters of two isolates. Clusters in which the first two case isolates were received at MDH on the same day were more likely to be solved than were clusters in which the first two case isolates were received over ⩾8 days. Investigation of clusters of ⩾3E. coliO157:H7 cases increased the success of cluster investigations.
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Di Mascolo, Luca, Tony Mroczkowski, Eugene Churazov, Emily Moravec, Mark Brodwin, Anthony Gonzalez, Bandon B. Decker, et al. "The Massive and Distant Clusters of WISE Survey." Astronomy & Astrophysics 638 (June 2020): A70. http://dx.doi.org/10.1051/0004-6361/202037818.
Abstract:
Context. The Massive and Distant Clusters of WISE Survey (MaDCoWS) provides a catalog of high-redshift (0.7 ≲ z ≲ 1.5) infrared-selected galaxy clusters. However, the verification of the ionized intracluster medium, indicative of a collapsed and nearly virialized system, is made challenging by the high redshifts of the sample members. Aims. The main goal of this work is to test the capabilities of the Atacama Compact Array (ACA; also known as the Morita Array) Band 3 observations, centered at about 97.5 GHz, to provide robust validation of cluster detections via the thermal Sunyaev–Zeldovich (SZ) effect. Methods. Using a pilot sample that comprises ten MaDCoWS galaxy clusters, accessible to ACA and representative of the median sample richness, we infer the masses of the selected galaxy clusters and respective detection significance by means of a Bayesian analysis of the interferometric data. Results. Our test of the Verification with the ACA – Localization and Cluster Analysis (VACA LoCA) program demonstrates that the ACA can robustly confirm the presence of the virialized intracluster medium in galaxy clusters previously identified in full-sky surveys. In particular, we obtain a significant detection of the SZ effect for seven out of the ten VACA LoCA clusters. We note that this result is independent of the assumed pressure profile. However, the limited angular dynamic range of the ACA in Band 3 alone, short observational integration times, and possible contamination from unresolved sources limit the detailed characterization of the cluster properties and the inference of the cluster masses within scales appropriate for the robust calibration of mass–richness scaling relations.
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Danganan, Alvincent Egonia, Ariel M. Sison, and Ruji P. Medina. "OCA: overlapping clustering application unsupervised approach for data analysis." Indonesian Journal of Electrical Engineering and Computer Science 14, no. 3 (June 1, 2019): 1471. http://dx.doi.org/10.11591/ijeecs.v14.i3.pp1471-1478.
Abstract:
<p>In this paper, a new data analysis tool called Overlapping Clustering Application (OCA) was presented. It was developed to identify overlapping clusters and outliers in an unsupervised manner. The main function of OCA is composed of three phases. The first phase is the detection of the abnormal values(outliers) in the datasets using median absolute deviation. The second phase is to segment data objects into cluster using k-means algorithm. Finally, the last phase is the identification of overlapping clusters, it uses maxdist (maximum distance of data objects allowed in a cluster) as a predictor of data objects that can belong to multiple clusters. Experimental results revealed that the developed OCA proved its capability in detecting overlapping clusters and outliers accordingly.</p>
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Käfer, Florian, Alexis Finoguenov, Dominique Eckert, Nicolas Clerc, Miriam E. Ramos-Ceja, Jeremy S. Sanders, and Vittorio Ghirardini. "Toward the low-scatter selection of X-ray clusters." Astronomy & Astrophysics 634 (January 28, 2020): A8. http://dx.doi.org/10.1051/0004-6361/201936131.
Abstract:
Context. One key ingredient in using galaxy clusters as a precision cosmological probe in large X-ray surveys is understanding selection effects. The dependence of the X-ray emission on the square of the gas density leads to a predominant role of cool cores in the detection of galaxy clusters. The contribution of cool cores to the X-ray luminosity does not scale with cluster mass and cosmology and therefore affects the use of X-ray clusters in producing cosmological constraints. Aims. One of the main science goals of the extended ROentgen Survey with an Imaging Telescope Array (eROSITA) mission is to constrain cosmology with a wide X-ray survey. We propose an eROSITA galaxy cluster detection scheme that avoids the use of X-ray cluster centers in detection. We calculate theoretical expectations and characterize the performance of this scheme by simulations. Methods. We performed Monte Carlo simulations of the upcoming eROSITA mission, including known foreground and background components. By performing realistic simulations of point sources in survey mode, we searched for spatial scales where the extended signal is not contaminated by the point-source flux. We derive a combination of scales and thresholds, which result in a clean extended source catalog. We designed the output of the cluster detection, which enables calibrating the core-excised luminosity using external mass measurements. We provide a way to incorporate the results of this calibration in producing the final core-excised luminosity. Results. Similarly to other galaxy cluster detection pipelines, we sample the detection space of the flux – cluster core radius of our method and find many similarities with the pipeline used in the 400d survey. Both detection methods require large statistics on compact clusters in order to reduce the contamination from point sources. The benefit of our pipeline consists of the sensitivity to the outer cluster shapes, which are characterized by large core sizes with little cluster to cluster variation at a fixed total mass of the cluster. Conclusions. Galaxy cluster detection through cluster outskirts improves the cluster characterization using eROSITA survey data and is expected to yield well-characterized cluster catalogs with simple selection functions.
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Xie, Yiqun, Xiaowei Jia, Shashi Shekhar, Han Bao, and Xun Zhou. "Significant DBSCAN+: Statistically Robust Density-based Clustering." ACM Transactions on Intelligent Systems and Technology 12, no. 5 (October 31, 2021): 1–26. http://dx.doi.org/10.1145/3474842.
Abstract:
Cluster detection is important and widely used in a variety of applications, including public health, public safety, transportation, and so on. Given a collection of data points, we aim to detect density-connected spatial clusters with varying geometric shapes and densities, under the constraint that the clusters are statistically significant. The problem is challenging, because many societal applications and domain science studies have low tolerance for spurious results, and clusters may have arbitrary shapes and varying densities. As a classical topic in data mining and learning, a myriad of techniques have been developed to detect clusters with both varying shapes and densities (e.g., density-based, hierarchical, spectral, or deep clustering methods). However, the vast majority of these techniques do not consider statistical rigor and are susceptible to detecting spurious clusters formed as a result of natural randomness. On the other hand, scan statistic approaches explicitly control the rate of spurious results, but they typically assume a single “hotspot” of over-density and many rely on further assumptions such as a tessellated input space. To unite the strengths of both lines of work, we propose a statistically robust formulation of a multi-scale DBSCAN, namely Significant DBSCAN+, to identify significant clusters that are density connected. As we will show, incorporation of statistical rigor is a powerful mechanism that allows the new Significant DBSCAN+ to outperform state-of-the-art clustering techniques in various scenarios. We also propose computational enhancements to speed-up the proposed approach. Experiment results show that Significant DBSCAN+ can simultaneously improve the success rate of true cluster detection (e.g., 10–20% increases in absolute F1 scores) and substantially reduce the rate of spurious results (e.g., from thousands/hundreds of spurious detections to none or just a few across 100 datasets), and the acceleration methods can improve the efficiency for both clustered and non-clustered data.
21
Jiongzhou, Liu, Li Jituo, and Lu Guodong. "Deformation similarity clustering based collision detection in clothing simulation." International Journal of Clothing Science and Technology 26, no. 5 (August 26, 2014): 395–411. http://dx.doi.org/10.1108/ijcst-08-2013-0095.
Abstract:
Purpose – The 3D dynamic clothing simulation is widely used in computer-added garment design. Collision detection and response are the essential component and also the efficiency bottleneck in the simulation. The purpose of this paper is to propose a high efficient collision detection algorithm for 3D clothing-human dynamic simulation to achieve both real-time and virtually real simulation effects. Design/methodology/approach – The authors approach utilizes the offline data learning results to simplify the online collision detection complexity. The approach includes two stages. In the off-line stage, model triangles with most similar deformations are first, partitioned into several near-rigid-clusters. Clusters from the clothing model and the human model are matched as pairs according to the fact that they hold the potential to intersect. For each cluster, a hierarchical bounding box tree is then constructed. In the on-line stage, collision detection is checked and treated parallelly inside each cluster pairs. A multiple task allocation strategy is proposed in parallel computation to ensure efficiency. Findings – Reasonably partitioning the 3D clothing and human model surfaces into several clusters and implementing collision detection on these cluster pairs can efficiently reduce the model primitive amounts that need be detected, consequently both improving the detection efficiency and remaining the simulation virtual effect. Originality/value – The current methods only utilize the dynamic clothing-human status; the authors algorithm furthermore combines the intrinsic correspondence relationship between clothing and human clusters to efficiently shrink the detection query scope to accelerate the detection speed. Moreover, partitioning the model into several independent clusters as detection units is much more profitable for parallel computation than current methods those treat the model entirety as the unit.
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E.Sateesh, E. Sateesh, and M. L. Prasanthi M.L.Prasanthi. "Classic Outlier Detection from Web Clusters using Disimilarity Measure." Paripex - Indian Journal Of Research 2, no. 3 (January 15, 2012): 98–101. http://dx.doi.org/10.15373/22501991/mar2013/37.
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Huang, Lan, Linda W. Pickle, David Stinchcomb, and Eric J. Feuer. "Detection of Spatial Clusters." Epidemiology 18, no. 1 (January 2007): 73–87. http://dx.doi.org/10.1097/01.ede.0000249994.30736.24.
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Held, Janis, Anna Beer, and Thomas Seidl. "Chain-detection Between Clusters." Datenbank-Spektrum 19, no. 3 (September 13, 2019): 219–30. http://dx.doi.org/10.1007/s13222-019-00324-9.
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Jokinen, T., M. Sipilä, H. Junninen, M. Ehn, G. Lönn, J. Hakala, T. Petäjä, R. L. Mauldin III, M. Kulmala, and D. R. Worsnop. "Atmospheric sulphuric acid and neutral cluster measurements using CI-APi-TOF." Atmospheric Chemistry and Physics Discussions 11, no. 12 (December 6, 2011): 31983–2002. http://dx.doi.org/10.5194/acpd-11-31983-2011.
Abstract:
Abstract. The first ambient measurements using nitrate ion based Chemical Ionization with the Atmospheric Pressure interface Time-Of-Flight mass spectrometer (CI-APi-TOF) for sulphuric acid and neutral cluster detection are presented. We have found CI-APi-TOF a highly stable and sensitive tool for molecular sulphuric acid detection. The lowest limit of detection for sulphuric acid was determined to be 3 × 104 molecules cm−3 for two hour averaging. Signals from sulphuric acid clusters up to tetramer accompanied by ammonia were also obtained but these were found to result from naturally charged clusters formed by ion induced clustering in the atmosphere during nucleation. Opposite to earlier studies with cluster mass spectrometers, we had no indication of neutral clusters. The reason is either less efficient charging of clusters in comparison to molecular sulphuric acid, or in low concentration of neutral clusters at our measurement site during these particular nucleation events. We show that utilizing high resolution mass spectrometry is crucial in separating the weak sulfuric acid cluster signal from the other compounds.
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Jokinen, T., M. Sipilä, H. Junninen, M. Ehn, G. Lönn, J. Hakala, T. Petäjä, R. L. Mauldin, M. Kulmala, and D. R. Worsnop. "Atmospheric sulphuric acid and neutral cluster measurements using CI-APi-TOF." Atmospheric Chemistry and Physics 12, no. 9 (May 9, 2012): 4117–25. http://dx.doi.org/10.5194/acp-12-4117-2012.
Abstract:
Abstract. The first ambient measurements using nitrate ion based Chemical Ionization with the Atmospheric Pressure interface Time-Of-Flight mass spectrometer (CI-APi-TOF) for sulphuric acid and neutral cluster detection are presented. We have found CI-APi-TOF a highly stable and sensitive tool for molecular sulphuric acid detection. The lowest limit of detection for sulphuric acid was determined to be 3.6 × 104 molecules cm−3 for 15 min averaging. Signals from sulphuric acid clusters up to tetramer containing ammonia were also obtained but these were found to result from naturally charged clusters formed by ion induced clustering in the atmosphere during nucleation. Opposite to earlier studies with cluster mass spectrometers, we had no indication of neutral clusters. The reason is either less efficient charging of clusters in comparison to molecular sulphuric acid, or the low concentration of neutral clusters at our measurement site during these particular nucleation events. We show that utilizing high resolution mass spectrometry is crucial in separating the weak sulfuric acid cluster signal from other compounds.
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Kitayama, Takumi, Masaya Kozuka, Yasuhiro Aruga, and Chikara Ichihara. "A Precise Method for Analysis of Elemental Distribution Inside Solute Clusters." Microscopy and Microanalysis 25, no. 2 (March 15, 2019): 349–55. http://dx.doi.org/10.1017/s1431927619000126.
Abstract:
AbstractA procedure to analyze the elemental concentration distribution inside solute clusters after detection of clusters from atom probe tomography data set was proposed. We developed a code which can directly illustrate an average concentration profile inside a cluster even in the case of including various sizes of ellipsoidal clusters. The profile can be with respect to absolute distance and includes errors in each data point. The reliability of the developed code was verified by analyzing an artificial cluster model which has inhomogeneous elemental distribution. It was found that the precise estimation of cluster centroids is important and that the preferable conditions for targeting clusters are a detection efficiency of over 20%, over 30 atoms in a cluster on average, and over 100 atoms for each concentration data point.
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Wang, Jing Hua, Xin Xiang Zhao, Peng Jin, and Guo Yan Zhang. "An Optimized Pruning-Based Outlier Detecting Algorithm." Applied Mechanics and Materials 411-414 (September 2013): 1076–80. http://dx.doi.org/10.4028/www.scientific.net/amm.411-414.1076.
Abstract:
An Optimized Pruning-based Outlier Detecting algorithm is proposed based on the density-based outlier detecting algorithm (LOF algorithm). The calculation accuracy and the time complexity of LOF algorithm are not ideal, so two steps are taken to reduce the amount of calculation and improve the calculation accuracy for LOF algorithm. Firstly, using cluster pruning technique to preprocess data set, at the same time filtering the non-outliers based on the differences of cluster models to avoid the error pruning of outliers located at the edge of clusters, different cluster models are output by inputing multiple parameters in the DBSCAN algorithm. Secondly,optimize the query process of the neighborhood (neighbor and k-neighbor). After pruning, local outlier factors are calculated only for the data objects out of clusters. Experimental results show that the algorithm proposed in this paper can improve the outlier detection accuracy, reduce the time complexity and realize the effective local outlier detection.
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Lubin, Lori M. "Clusters in the Optical." Highlights of Astronomy 13 (2005): 286–90. http://dx.doi.org/10.1017/s1539299600015811.
Abstract:
AbstractI present a brief review of studies of galaxy clusters in the optical. Clusters of galaxies were historically detected in the optical, and this selection provided the first large, statistical samples of clusters. I describe how these samples have been instrumental in characterizing the properties of the local cluster population, tracing large scale structure, and constraining cosmology. More sophisticated cluster detection techniques in the optical have now made it possible to detect large numbers of clusters up to z ~ 1.4. I describe these advances and discuss how large-area and deep surveys are being used to determine the evolution in the global cluster properties and the properties of cluster galaxy populations.
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Fang, Changjian, Dejun Mu, Zhenghong Deng, Jun Hu, and Chen-He Yi. "Fast detection of the fuzzy communities based on leader-driven algorithm." International Journal of Modern Physics B 32, no. 06 (February 26, 2018): 1850058. http://dx.doi.org/10.1142/s0217979218500583.
Abstract:
In this paper, we present the leader-driven algorithm (LDA) for learning community structure in networks. The algorithm allows one to find overlapping clusters in a network, an important aspect of real networks, especially social networks. The algorithm requires no input parameters and learns the number of clusters naturally from the network. It accomplishes this using leadership centrality in a clever manner. It identifies local minima of leadership centrality as followers which belong only to one cluster, and the remaining nodes are leaders which connect clusters. In this way, the number of clusters can be learned using only the network structure. The LDA is also an extremely fast algorithm, having runtime linear in the network size. Thus, this algorithm can be used to efficiently cluster extremely large networks.
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Bogdán, Ákos, Lorenzo Lovisari, Patrick Ogle, Orsolya E. Kovács, Thomas Jarrett, Christine Jones, William R. Forman, and Lauranne Lanz. "Detection of a Superluminous Spiral Galaxy in the Heart of a Massive Galaxy Cluster." Astrophysical Journal 930, no. 2 (May 1, 2022): 138. http://dx.doi.org/10.3847/1538-4357/ac62cd.
Abstract:
Abstract It is well established that brightest cluster galaxies (BCGs), residing in the centers of galaxy clusters, are typically massive and quenched galaxies with cD or elliptical morphology. An optical survey suggested that an exotic galaxy population, superluminous spiral and lenticular galaxies, could be the BCGs of some galaxy clusters. Because the cluster membership and the centroid of a cluster cannot be accurately determined based solely on optical data, we followed up a sample of superluminous disk galaxies and their environments using XMM-Newton X-ray observations. Specifically, we explored seven superluminous spiral and lenticular galaxies that are candidate BCGs. We detected massive galaxy clusters around five superluminous disk galaxies and established that one superluminous spiral, 2MASX J16273931+3002239, is the central BCG of a galaxy cluster. The temperature and total mass of the cluster are kT 500 = 3.55 − 0.20 + 0.18 keV and M 500 = (2.39 ± 0.19) × 1014 M ⊙. We identified the central galaxies of the four clusters that do not host superluminous disk galaxies at their cores, and established that the centrals are massive elliptical galaxies. However, for two of the clusters, the offset superluminous spirals are brighter than the central galaxies, implying that the superluminous disk galaxies are the brightest cluster galaxies. Our results demonstrate that superluminous disk galaxies are rarely the central systems of galaxy clusters. This is likely because galactic disks are destroyed by major mergers, which are more frequent in high-density environments. We speculate that the disks of superluminous disk galaxies in cluster cores may have been reformed due to mergers with gas-rich satellites.
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Safonova, Margarita, and Sohrab Rahvar. "Detection of IMBHs from microlensing in globular clusters." Proceedings of the International Astronomical Union 2, S238 (August 2006): 439–40. http://dx.doi.org/10.1017/s1743921307005844.
Abstract:
AbstractGlobular clusters have been long predicted to host intermediate-mass black holes (IMBHs) in their centres. The growing evidence that some/all Galactic globular clusters (GCs) could harbour middle range (102 – 104M⊙) black holes, just as galaxies do, stimulates the searches and the development of new methods for proving their existence. Here we propose another method of detection – the microlensing of the cluster stars by the central BH.
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Nenashev, Vadim A., Igor G. Khanykov, and Mikhail V. Kharinov. "A Model of Pixel and Superpixel Clustering for Object Detection." Journal of Imaging 8, no. 10 (October 6, 2022): 274. http://dx.doi.org/10.3390/jimaging8100274.
Abstract:
The paper presents a model of structured objects in a grayscale or color image, described by means of optimal piecewise constant image approximations, which are characterized by the minimum possible approximation errors for a given number of pixel clusters, where the approximation error means the total squared error. An ambiguous image is described as a non-hierarchical structure but is represented as an ordered superposition of object hierarchies, each containing at least one optimal approximation in g0 = 1,2,..., etc., colors. For the selected hierarchy of pixel clusters, the objects-of-interest are detected as the pixel clusters of optimal approximations, or as their parts, or unions. The paper develops the known idea in cluster analysis of the joint application of Ward’s and K-means methods. At the same time, it is proposed to modernize each of these methods and supplement them with a third method of splitting/merging pixel clusters. This is useful for cluster analysis of big data described by a convex dependence of the optimal approximation error on the cluster number and also for adjustable object detection in digital image processing, using the optimal hierarchical pixel clustering, which is treated as an alternative to the modern informally defined “semantic” segmentation.
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Smyser, Timothy J., Richard J. Guenzel, Christopher N. Jacques, and Edward O. Garton. "Double-observer evaluation of pronghorn aerial line-transect surveys." Wildlife Research 43, no. 6 (2016): 474. http://dx.doi.org/10.1071/wr16006.
Abstract:
Context Distance sampling is used to estimate abundance for several taxa, including pronghorn (Antilocapra americana). Comparisons between population estimates derived from quadrat sampling and distance sampling suggest that distance sampling underestimates pronghorn density, likely owing to violations of the critical assumption of distance sampling that all pronghorn within the innermost distance band (A band; nearest to the aircraft) are detected. Aims We sought to rigorously test the assumption that all pronghorn clusters are detected within the innermost distance band by applying a double-observer approach to an established pronghorn aerial-survey protocol. Additionally, we evaluated potential effects of cluster size, landscape composition and seat position (front seat versus rear) on the probability of detection. Methods We conducted aerial line-transect distance-sampling surveys using independent, paired observers and modelled the probability of detection with mark–recapture distance-sampling (MRDS) analysis techniques that explicitly estimate the probability of detection for pronghorn clusters in the innermost distance band. We compared density estimates produced by the MRDS analysis with those produced by multiple-covariate distance sampling (MCDS), a method that assumes complete detection for clusters on the transect line. Key results We identified violations of the assumption that all clusters within the innermost distance band were detected, which would contribute to proportional biases in density estimates for analysis techniques that assume complete detection. The frequency of missed clusters was modest from the front-seat position, with 45 of the 47 (96%) clusters in the A band detected. In contrast, the frequency of missed clusters was more substantial for the rear position, from which 37 of 47 (79%) clusters in the A band were detected. Further, our analysis showed that cluster size and landscape composition were important factors for pronghorn sightability. Conclusions When implementing standard survey methodologies, pronghorn aerial-line transect surveys underestimated population densities. A double-observer survey configuration allowed us to quantify and correct for the bias caused by the failure of observers to detect all pronghorn clusters within the innermost distance band. Implications Population monitoring programs should incorporate double-observer validation trials to quantify the extent of bias owing to undetected clusters within the innermost distance band realised under typical survey conditions. Wildlife managers can improve the precision of pronghorn aerial line-transect surveys by incorporating cluster size and measures of landscape composition and complexity into detection models without incurring additional survey costs.
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Filippi, Margaux, Irina I. Rypina, Alireza Hadjighasem, and Thomas Peacock. "An Optimized-Parameter Spectral Clustering Approach to Coherent Structure Detection in Geophysical Flows." Fluids 6, no. 1 (January 12, 2021): 39. http://dx.doi.org/10.3390/fluids6010039.
Abstract:
In Lagrangian dynamics, the detection of coherent clusters can help understand the organization of transport by identifying regions with coherent trajectory patterns. Many clustering algorithms, however, rely on user-input parameters, requiring a priori knowledge about the flow and making the outcome subjective. Building on the conventional spectral clustering method of Hadjighasem et al. (2016), a new optimized-parameter spectral clustering approach is developed that automatically identifies optimal parameters within pre-defined ranges. A noise-based metric for quantifying the coherence of the resulting coherent clusters is also introduced. The optimized-parameter spectral clustering is applied to two benchmark analytical flows, the Bickley Jet and the asymmetric Duffing oscillator, and to a realistic, numerically generated oceanic coastal flow. In the latter case, the identified model-based clusters are tested using observed trajectories of real drifters. In all examples, our approach succeeded in performing the partition of the domain into coherent clusters with minimal inter-cluster similarity and maximum intra-cluster similarity. For the coastal flow, the resulting coherent clusters are qualitatively similar over the same phase of the tide on different days and even different years, whereas coherent clusters for the opposite tidal phase are qualitatively different.
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Erly, Steven J., Joshua T. Herbeck, Roxanne P. Kerani, and Jennifer R. Reuer. "Characterization of Molecular Cluster Detection and Evaluation of Cluster Investigation Criteria Using Machine Learning Methods and Statewide Surveillance Data in Washington State." Viruses 12, no. 2 (January 26, 2020): 142. http://dx.doi.org/10.3390/v12020142.
Abstract:
Molecular cluster detection can be used to interrupt HIV transmission but is dependent on identifying clusters where transmission is likely. We characterized molecular cluster detection in Washington State, evaluated the current cluster investigation criteria, and developed a criterion using machine learning. The population living with HIV (PLWH) in Washington State, those with an analyzable genotype sequences, and those in clusters were described across demographic characteristics from 2015 to2018. The relationship between 3- and 12-month cluster growth and demographic, clinical, and temporal predictors were described, and a random forest model was fit using data from 2016 to 2017. The ability of this model to identify clusters with future transmission was compared to Centers for Disease Control and Prevention (CDC) and the Washington state criteria in 2018. The population with a genotype was similar to all PLWH, but people in a cluster were disproportionately white, male, and men who have sex with men. The clusters selected for investigation by the random forest model grew on average 2.3 cases (95% CI 1.1–1.4) in 3 months, which was not significantly larger than the CDC criteria (2.0 cases, 95% CI 0.5–3.4). Disparities in the cases analyzed suggest that molecular cluster detection may not benefit all populations. Jurisdictions should use auxiliary data sources for prediction or continue using established investigation criteria.
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Shoji, Tomokazu, Natsu Sato, Haruhisa Fukuda, Yuichi Muraki, Keishi Kawata, and Manabu Akazawa. "Clinical Implication of the Relationship between Antimicrobial Resistance and Infection Control Activities in Japanese Hospitals: A Principal Component Analysis-Based Cluster Analysis." Antibiotics 11, no. 2 (February 10, 2022): 229. http://dx.doi.org/10.3390/antibiotics11020229.
Abstract:
There are few multicenter investigations regarding the relationship between antimicrobial resistance (AMR) and infection-control activities in Japanese hospitals. Hence, we aimed to identify Japanese hospital subgroups based on facility characteristics and infection-control activities. Moreover, we evaluated the relationship between AMR and hospital subgroups. We conducted a cross-sectional study using administrative claims data and antimicrobial susceptibility data in 124 hospitals from April 2016 to March 2017. Hospitals were classified using cluster analysis based the principal component analysis-transformed data. We assessed the relationship between each cluster and AMR using analysis of variance. Ten variables were selected and transformed into four principal components, and five clusters were identified. Cluster 5 had high infection control activity. Cluster 2 had partially lower activity of infection control than the other clusters. Clusters 3 and 4 had a higher rate of surgeries than Cluster 1. The methicillin-resistant Staphylococcus aureus (MRSA)/S. aureus detection rate was lowest in Cluster 1, followed, respectively, by Clusters 5, 2, 4, and 3. The MRSA/S. aureus detection rate differed significantly between Clusters 4 and 5 (p = 0.0046). Our findings suggest that aggressive examination practices are associated with low AMR whereas surgeries, an infection risk factor, are associated with high AMR.
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Duan, Guiqin, and Chensong Zou. "A clustering effectiveness measurement model based on merging similar clusters." PeerJ Computer Science 10 (February 29, 2024): e1863. http://dx.doi.org/10.7717/peerj-cs.1863.
Abstract:
This article presents a clustering effectiveness measurement model based on merging similar clusters to address the problems experienced by the affinity propagation (AP) algorithm in the clustering process, such as excessive local clustering, low accuracy, and invalid clustering evaluation results that occur due to the lack of variety in some internal evaluation indices when the proportion of clusters is very high. First, depending upon the “rough clustering” process of the AP clustering algorithm, similar clusters are merged according to the relationship between the similarity between any two clusters and the average inter-cluster similarity in the entire sample set to decrease the maximum number of clusters Kmax. Then, a new scheme is proposed to calculate intra-cluster compactness, inter-cluster relative density, and inter-cluster overlap coefficient. On the basis of this new method, several internal evaluation indices based on intra-cluster cohesion and inter-cluster dispersion are designed. Results of experiments show that the proposed model can perform clustering and classification correctly and provide accurate ranges for clustering using public UCI and NSL-KDD datasets, and it is significantly superior to the three improved clustering algorithms compared with it in terms of intrusion detection indices such as detection rate and false positive rate (FPR).
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West, Michael J., Patrick Côté, Henry C. Ferguson, Michael D. Gregg, Andrés Jordán, Ronald O. Marzke, Nial R. Tanvir, and Ted von Hippel. "Intergalactic Globular Clusters." Highlights of Astronomy 13 (2005): 175–76. http://dx.doi.org/10.1017/s1539299600015513.
Abstract:
AbstractWe confirm and extend our previous detection of a population of intergalactic globular clusters in Abell 1185, and report the first discovery of an intergalactic globular cluster in the nearby Virgo cluster of galaxies. The numbers, colors and luminosities of these objects can place constraints on their origin, which in turn may yield new insights to the evolution of galaxies in dense environments.
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Bevington, Connor WJ, Ju-Chieh (Kevin) Cheng, Ivan S. Klyuzhin, Mariya V. Cherkasova, Catharine A. Winstanley, and Vesna Sossi. "A Monte Carlo approach for improving transient dopamine release detection sensitivity." Journal of Cerebral Blood Flow & Metabolism 41, no. 1 (February 12, 2020): 116–31. http://dx.doi.org/10.1177/0271678x20905613.
Abstract:
Current methods using a single PET scan to detect voxel-level transient dopamine release—using F-test (significance) and cluster size thresholding—have limited detection sensitivity for clusters of release small in size and/or having low release levels. Specifically, simulations show that voxels with release near the peripheries of such clusters are often rejected—becoming false negatives and ultimately distorting the F-distribution of rejected voxels. We suggest a Monte Carlo method that incorporates these two observations into a cost function, allowing erroneously rejected voxels to be accepted under specified criteria. In simulations, the proposed method improves detection sensitivity by up to 50% while preserving the cluster size threshold, or up to 180% when optimizing for sensitivity. A further parametric-based voxelwise thresholding is then suggested to better estimate the release dynamics in detected clusters. We apply the Monte Carlo method to a pilot scan from a human gambling study, where additional parametrically unique clusters are detected as compared to the current best methods—results consistent with our simulations.
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Ma, Shu Hua, Jin Kuan Wang, Zhi Gang Liu, and Hou Yan Jiang. "Density-Based Distributed Elliptical Anomaly Detection in Wireless Sensor Networks." Applied Mechanics and Materials 249-250 (December 2012): 226–30. http://dx.doi.org/10.4028/www.scientific.net/amm.249-250.226.
Abstract:
Data measured and collected by WSNs is often unreliable and a big amount of anomaly data exist. Detecting these anomaly in energy-constrained situations is an important challenge in managing these types of networks. To detect anomalies induced by the decrease of battery power, we use HyCARCE algorithm but it has the problem of low detection rate and high false positive rate when the input space consists of a mixture of dense and sparse regions which make the anomalies form clusters. The paper presents a density-based algorithm to separate the normal cluster from all clusters. The performance of this algorithm is tested on a subset of the data gathered from a real sensor network deployed at the Intel Berkeley Research Laboratory in the USA and this density-based method has a better detection performance than HyCARCE algorithm.
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Gran, F., M. Zoccali, R. Contreras Ramos, E. Valenti, A. Rojas-Arriagada, J. A. Carballo-Bello, J. Alonso-García, D. Minniti, M. Rejkuba, and F. Surot. "Globular cluster candidates in the Galactic bulge: Gaia and VVV view of the latest discoveries." Astronomy & Astrophysics 628 (August 2019): A45. http://dx.doi.org/10.1051/0004-6361/201834986.
Abstract:
Context. Thanks to the recent wide-area photometric surveys, the number of star cluster candidates have risen exponentially in the last few years. Most detections, however, are based only on the presence of an overdensity of stars in a given region or an overdensity of variable stars, regardless of their distance. As candidates, their detection has not been dynamically confirmed. Therefore, it is currently unknown how many and which of the published candidates are true clusters and which are chance alignments. Aims. We present a method to detect and confirm star clusters based on the spatial distribution, coherence in motion, and appearance on the color-magnitude diagram. We explain and apply this approach to one new star cluster and several candidate star clusters published in the literature. Methods. The presented method is based on data from the second data release of Gaia complemented with data from the VISTA Variables in the Vía Láctea survey for the innermost bulge regions. This method consists of a nearest neighbors algorithm applied simultaneously over spatial coordinates, star color, and proper motions to detect groups of stars that are close in the sky, move coherently, and define narrow sequences in the color-magnitude diagram, such as a young main sequence or a red giant branch. Results. When tested in the bulge area (−10 < ℓ (deg) < +10; −10 < b (deg) < +10) the method successfully recovered several known young and old star clusters. We report in this work the detection of one new, likely old star cluster, while deferring the others to a forthcoming paper. Additionally, the code has been applied to the position of 93 candidate star clusters published in the literature. As a result, only two of these clusters are confirmed as coherently moving groups of stars at their nominal positions.
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Grosbøl, Preben, and Horacio Dottori. "Comparing ALMA, VLT, and HST data for Massive, Young Clusters in Grand-Design Spirals." Proceedings of the International Astronomical Union 12, S316 (August 2015): 141–42. http://dx.doi.org/10.1017/s1743921316007080.
Abstract:
AbstractA population of young, massive stellar cluster complexes with near-infrared (NIR) colors indicating high extinction (i.e. Av ~ 7m) was identified on HAWK-I/VLT images of several nearby, grand-design spiral galaxies. Models suggest that they are very young cluster complexes still embedded in a dust/gas envelope which will be expelled after 5-7 Myr. This type of very young, embedded clusters are not seen in optical studies using HST data.A detailed comparison of HST and HAWK-I images was done to better understand the discrepancy between the optical and NIR detection of stellar clusters in nearby galaxies. More than 70% of the NIR clusters are located close to dust lanes which would make an optical detection difficult. A comparison of the ALMA CO(1-0)-map of NGC 4321 and the young, massive clusters shows that 60% of them have CO emission within 2“ indicating a correlation between giant molecular clouds and formation of massive clusters.
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Klesman, Alison J., and Vicki L. Sarajedini. "Multiwavelength Detection of Cluster AGN." Proceedings of the International Astronomical Union 5, S267 (August 2009): 112. http://dx.doi.org/10.1017/s1743921310005739.
Abstract:
AbstractWe present the initial results of a multiwavelength study to detect AGN in several galaxy clusters at moderate redshifts (0.5 < z < 0.9). By using multiple epochs of HST data, we identify 10–15 optically variable galactic nuclei in each cluster. The variable and non-variable galaxies are compared with X-ray point sources.
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Jin, Xin, Hui Quan Wang, and Zhong He Jin. "Anomaly detection of satellite telemetry data based on extended dominant sets clustering." Journal of Physics: Conference Series 2489, no. 1 (May 1, 2023): 012036. http://dx.doi.org/10.1088/1742-6596/2489/1/012036.
Abstract:
Abstract To mine out anomalies in satellite telemetry data under unsupervised conditions, a cluster-based method is proposed in this paper. Firstly, an extended dominant sets clustering algorithm is proposed to cluster the telemetry data with arbitrary shapes. Secondly, objects that do not belong to any cluster or belong to small clusters are traditionally identified as anomalies. Thirdly, the anomalies in large clusters are detected according to the relative similarity. Finally, the information on anomaly windows in the telemetry sequence is obtained according to the local anomaly rate, which provides more characteristics of the anomalies. Experimental results show that: 1) The proposed extended dominant sets clustering algorithm can deal with the dataset containing multiple and arbitrarily shaped clusters; 2) The introduction of relative similarity increases the AUC values of anomaly detection by 3%~10%; 3) The proposed anomaly detection method can effectively detect the anomalies in magnetometer telemetry data of Tianping-2B satellite. Therefore, the proposed method can achieve anomaly detection of satellite telemetry data under unsupervised conditions, and provide support for improving satellite safety and reliability.
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Kavitha, R., C. kotteeswari, and G. Sumathi. "Memory Leak Detection in Clusters." International Journal of Computer Applications 3, no. 1 (June 10, 2010): 13–16. http://dx.doi.org/10.5120/702-985.
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Martin, T. P., T. Bergmann, U. Näher, H. Schaber, and U. Zimmermann. "Detection of large fullerene clusters." Nuclear Instruments and Methods in Physics Research Section B: Beam Interactions with Materials and Atoms 88, no. 1-2 (April 1994): 1–5. http://dx.doi.org/10.1016/0168-583x(94)96070-4.
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R., Yasir Abdullah, Mary Posonia A., and Barakkath Nisha U. "A Graph Correlated Anomaly Detection with Fuzzy Model for Distributed Wireless Sensor Networks." International Journal of Electrical and Electronic Engineering & Telecommunications 12, no. 5 (2023): 306–16. http://dx.doi.org/10.18178/ijeetc.12.5.306-316.
Abstract:
Wireless sensor networks have limited power for processing data, storage, and communication. Due to power shortages and anonymous attacks, sensor nodes may produce faulty or anomaly data which affects the accuracy of the entire system. Effective anomaly detection is essential to make an accurate prediction of the result. Moreover, clustering-based anomaly detection reduces energy consumption by avoiding individual sensory data reporting to the base station. The proposed methodology consists of two phases: Correlated graph clustering, and anomaly detection using a Fuzzy model. In the first phase, the spatial correlation of the sensor readings is used to generate a graph, partitioned into clusters. The intra-cluster and inter-cluster temporal correlations are analyzed to refine the optimized cluster structure. Finally, a fuzzy Mamdani model is used to classify the clusters as either normal or anomalous based on their membership values. The proposed approach leverages both spatial and temporal correlation between sensor measurements to form optimized clusters that are more effective for anomaly detection. The Experiments performed on a real-world dataset of WSNs indicate the efficacy of the proposed methodology, which shows significant improvement over traditional anomaly detection methods the electronic file of your paper will be formatted further for final publication.
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Hao, ZiQi, ZhenJiang Zhang, and Han-Chieh Chao. "A Cluster-Based Fuzzy Fusion Algorithm for Event Detection in Heterogeneous Wireless Sensor Networks." Journal of Sensors 2015 (2015): 1–11. http://dx.doi.org/10.1155/2015/641235.
Abstract:
As limited energy is one of the tough challenges in wireless sensor networks (WSN), energy saving becomes important in increasing the lifecycle of the network. Data fusion enables combining information from several sources thus to provide a unified scenario, which can significantly save sensor energy and enhance sensing data accuracy. In this paper, we propose a cluster-based data fusion algorithm for event detection. We usek-means algorithm to form the nodes into clusters, which can significantly reduce the energy consumption of intracluster communication. Distances between cluster heads and event and energy of clusters are fuzzified, thus to use a fuzzy logic to select the clusters that will participate in data uploading and fusion. Fuzzy logic method is also used by cluster heads for local decision, and then the local decision results are sent to the base station. Decision-level fusion for final decision of event is performed by base station according to the uploaded local decisions and fusion support degree of clusters calculated by fuzzy logic method. The effectiveness of this algorithm is demonstrated by simulation results.
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Moss, Maxamillian A. N., Dagen D. Hughes, Ian Crawford, Martin W. Gallagher, Michael J. Flynn, and David O. Topping. "Comparative Analysis of Traditional and Advanced Clustering Techniques in Bioaerosol Data: Evaluating the Efficacy of K-Means, HCA, and GenieClust with and without Autoencoder Integration." Atmosphere 14, no. 9 (September 8, 2023): 1416. http://dx.doi.org/10.3390/atmos14091416.
Abstract:
In a comparative study contrasting new and traditional clustering techniques, the capabilities of K-means, the hierarchal clustering algorithm (HCA), and GenieClust were examined. Both K-means and HCA demonstrated strong consistency in cluster profiles and sizes, emphasizing their effectiveness in differentiating particle types and confirming that the fundamental patterns within the data were captured reliably. An added dimension to the study was the integration of an autoencoder (AE). When coupled with K-means, the AE enhanced outlier detection, particularly in identifying compositional loadings of each cluster. Conversely, whilst the AE’s application to all methods revealed a potential for noise reduction by removing infrequent, larger particles, in the case of HCA, this information distortion during the encoding process may have affected the clustering outcomes by reducing the number of observably distinct clusters. The findings from this study indicate that GenieClust, when applied both with and without an AE, was effective in delineating a notable number of distinct clusters. Furthermore, each cluster’s compositional loadings exhibited greater internal variability, distinguishing up to 3× more particle types per cluster compared to traditional means, and thus underscoring the algorithms’ ability to differentiate subtle data patterns. The work here postulates that the application of GenieClust both with and without an AE may provide important information through initial outlier detection and enriched speciation with an AE applied, evidenced by a greater number of distinct clusters within the main body of the data.