Relevant bibliographies by topics / Advanced Cluster Analysis

Academic literature on the topic 'Advanced Cluster Analysis'

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Published: 27 July 2024
Last updated: 29 July 2024

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Journal articles on the topic "Advanced Cluster Analysis"

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DAGIM, S., E. V. PUSTOVALOV, A. N. FEDORETS, and A. M. FROLOV. "EXPLORING AMORPHOUS ALLOYS: ADVANCED ELECTRON MICROSCOPY AND CLUSTER ANALYSIS." Computational nanotechnology 11, no. 1 (March 30, 2024): 112–20. http://dx.doi.org/10.33693/2313-223x-2024-11-1-112-120.

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In this study, we explored the atomic structure and orderliness of amorphous alloys through advanced electron microscopy and analytical techniques. Amorphous alloys, characterized by disordered atomic structures, exhibit promising applications in technology. The research addresses a crucial knowledge gap by investigating cluster distribution, particle arrangement, and orderliness within the amorphous matrix. High-resolution electron microscopy (HREM) images are analyzed using diverse algorithms and software tools. The study establishes a correlation between angles approaching 180 degrees and increased orderliness within clusters, highlighting the reliability of angle distribution analysis. Robust indicators, including Div (SP(B/V)) and Div (Mu(B/V)) metrics, assess and compare amorphous alloy samples. Kullback-Leibler (K-L) divergence indicates the significance of cluster ordering, validated by the S-K test. Radial Distribution Function (RDF) analysis uncovers local short-range order, deepening understanding despite limited orderliness discernment. These findings not only enhance our understanding of metallic glasses or amorphous alloys but also offer opportunities for tailored design and improved applications across various technological domains.
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Feng, Guohuan, Junchen Lin, and Keyi Wang. "Researches Advanced in Clustering Algorithms." Highlights in Science, Engineering and Technology 16 (November 10, 2022): 168–77. http://dx.doi.org/10.54097/hset.v16i.2498.

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Clustering is a technique to find the intrinsic structure between data and is a fundamental problem in many data-driven application fields. Currently, clustering is generally modeled as an unsupervised learning task, aiming to mine similar features between different samples and cluster samples with similar features into clusters. Ideally, objects in the same cluster are expected to be similar in the clustering results, while objects in different clusters are quite different. This study summarizes the research status of clustering algorithms in recent years. Specifically, the relevant critical steps of clustering algorithms are first introduced. From two aspects of partition and hierarchical clustering, representative clustering algorithms such as K-means, K-medoids, CLARANS, BIRCH, DBSCAN, and CURE are further detailed. This study also analyzes and summarizes the above algorithms in terms of critical technologies, algorithm ideas, benefits, and shortcomings and compares the distance accuracy of different algorithms on standard data sets. The above work will provide a valuable reference for cluster analysis and data mining research.
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Yadav, Pradeep Kumar, R. S. Sikarwar, Badal Verma, Sushma Tiwari, and D. K. Shrivastava. "Genetic Divergence for Grain Yield and Its Components in Bread Wheat (Triticum aestivum L.): Experimental Investigation." International Journal of Environment and Climate Change 13, no. 5 (April 1, 2023): 340–48. http://dx.doi.org/10.9734/ijecc/2023/v13i51776.

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The present investigation Comprises 34 advanced breeding lines including checks of bread wheat and experiment was conducted in a complete randomized block design with three replications at the research farm department of genetics and plant breeding, RVSKVV, B.M. College of Agriculture, Khandwa during Rabi season (November 2021 to April 2022) for estimation of the multivariate analysis of divergence. The advanced breeding lines were grouped into seven clusters. Cluster III contained the highest number of advanced breeding lines(12) and clusters V, VI, and VII contained the lowest (1 each). The inter-cluster distance in most cases was larger than the intra cluster distance which indicated that wider diversity is present among the advanced breeding lines of distant grouped. The highest intra cluster distance was observed in cluster IV revealed maximum genetic divergence among its constituents. The highest inter-cluster distance was found between cluster VI and VII and the lowest was between cluster V and VI. Highest cluster mean exhibited in cluster VII for most of the agro-morphological traits i.e. number of tillers/plant, spike length, spike weight, number of grain/spike followed by cluster II for grain filling period, days to maturity and plant height. On the basis of genetic diversity analysis, maximum percent contribution towards genetic divergence in 34 advanced breeding lines were found in grain filling period, days to maturity, number of grain/spike, days to 50% flowering, biological yield per plant and harvest index. Such differences in the genetic component of traits studied in the manuscript can be applied as a source of variation in other breeding programmes and crossing nurseries for wheat improvement.
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Siddika, A., A. K. M. Aminul Islam, M. G. Rasul, M. A. K. Mian, and J. U. Ahmed. "GENETIC DIVERSITY IN ADVANCED GENERATION OF VEGETABLE PEA (Pisum sativum L.)." Bangladesh Journal of Plant Breeding and Genetics 27, no. 1 (July 8, 2015): 9–16. http://dx.doi.org/10.3329/bjpbg.v27i1.23972.

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Twenty five advanced lines among them twelve lines obtained from the cross between Edible Podded Pea and IPSA Motorsuty-1, nine obtained from the cross between Local White and IPSA Motorsuty-3 and five parental lines were included to measure genetic diversity. The field experiment was conducted at the research farm, Bangabandhu Sheikh Mujibur Rahman Agricultural University, Gazipur, Bangladesh. Analysis of variance showed significant differences among the genotypes for all characters. Multivariate analysis based 14 agronomic characters indicated that the 26 genotypes fell into five distant clusters. Fifty percent germination was found to be contributed maximum towards the total divergence. The inter cluster (D2 values) distance was maximum between cluster I and cluster II and intra-cluster distance was in cluster III. Cluster V comprising five genotypes, namely, G11, G14, G21, G22, G25 and scored first position for 50% germination, pod per plant, 100 green seed weight and seed yield per plant (6.02). Genotypes belonging to the cluster I, II and V having greater inter cluster distance and higher cluster means for various characters could be recommended for inclusion in future breeding program as they are expected to produce good segregates.
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Streur, Megan, Sarah J. Ratcliffe, David Callans, M. Benjamin Shoemaker, and Barbara Riegel. "Atrial fibrillation symptom clusters and associated clinical characteristics and outcomes: A cross-sectional secondary data analysis." European Journal of Cardiovascular Nursing 17, no. 8 (May 22, 2018): 707–16. http://dx.doi.org/10.1177/1474515118778445.

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Background: Symptom clusters among adults with atrial fibrillation have previously been identified but no study has examined the relationship between symptom clusters and outcomes. Aims: The purpose of this study was to identify atrial fibrillation-specific symptom clusters, characterize individuals with each cluster, and determine whether symptom cluster membership is associated with healthcare utilization. Methods: This was a cross-sectional secondary data analysis of 1501 adults from the Vanderbilt Atrial Fibrillation Registry with verified atrial fibrillation. Self-reported symptoms were measured with the University of Toronto Atrial Fibrillation Severity Scale. We used hierarchical cluster analysis (Ward’s method) to identify clusters and dendrograms, pseudo F, and pseudo T-squared to determine the ideal number of clusters. Next, we used regression analysis to examine the association between cluster membership and healthcare utilization. Results: Males predominated (67%) and the average age was 58.4 years. Two symptom clusters were identified, a Weary cluster (3.7%, n=56, fatigue at rest, shortness of breath at rest, chest pain, and dizziness) and an Exertional cluster (32.7%, n=491, shortness of breath with activity and exercise intolerance). Several sociodemographic and clinical characteristics varied by symptom cluster group membership, including age, gender, atrial fibrillation type, body mass index, comorbidity status, and treatment strategy. Women were more likely to experience either cluster ( p<0.001). The Weary cluster was associated with nearly triple the rate of emergency department utilization (incident rate ratio [IRR] 2.8, p<0.001) and twice the rate of hospitalizations (IRR 1.9, p<0.001). Conclusion: We identified two symptom clusters. The Weary cluster was associated with a significantly increased rate of healthcare utilization.
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Dalcin, Thais, Alsones Balestrin, and Eduardo Künzel Teixeira. "Start-Up Cluster Development: A Multi-Case Analysis in the Brazilian Context." International Journal of Innovation and Technology Management 14, no. 06 (November 9, 2017): 1750035. http://dx.doi.org/10.1142/s0219877017500353.

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This paper aims to contribute to knowledge on start-up cluster development by describing the core resource configurations in the development trajectory of start-up clusters in an emergent country. This research involves two start-up clusters in the Brazilian context. The results provide a framework formed by four stages that indicates the trajectory of start-up cluster development from the basic level, in which the most evident resources are tangible and endogenous, to the more advanced level, which comprises intangible and exogenous resources. In the fourth stage, start-up clusters improve resources, such as quality of life, cultural diversity and political and economic conditions to foster entrepreneurship and innovation.
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Randler, Christoph. "An Analysis of Heterogeneity in German Speaking Birdwatchers Reveals Three Distinct Clusters and Gender Differences." Birds 2, no. 3 (July 29, 2021): 250–60. http://dx.doi.org/10.3390/birds2030018.

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The purpose of this study was to segment birdwatchers into clusters. Members from a wide range of bird related organizations, from highly specialized birders as well as Facebook bird group members were studied to provide a diverse dataset (n = 2766; 50.5% men). Birding specialization was measured with a battery of questionnaires. Birding specialization encompassed the three constructs of skill/competence, behavior, personal and behavioral commitment. Additionally, involvement, measured by centrality to lifestyle, attraction, social bonding, and identity, was used. The NbClust analyses showed that a three-cluster solution was the optimal solution. Then, k-means cluster analysis was applied on three groups: casual/novice, intermediate, and specialist/advanced birdwatchers. More men than women were in the specialist/advanced group and more women than men in the casual/novice group. As a conclusion, this study confirms a three-cluster solution for segmenting German birdwatchers based on a large and diverse sample and a broad conceptualization of the construct birding specialization. These data can be used to address different target audiences (novices, advanced birders) with different programs, e.g., in nature conservation.
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Bates, Ian, Sherly Meilianti, Lina Bader, Rishi Gandhi, Rachael Leng, and Kirsten Galbraith. "Strengthening Primary Healthcare through accelerated advancement of the global pharmacy workforce: a cross-sectional survey of 88 countries." BMJ Open 12, no. 5 (May 2022): e061860. http://dx.doi.org/10.1136/bmjopen-2022-061860.

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ObjectiveAdvancing the pharmacy workforce contributes to strengthening primary healthcare and accelerating progress towards universal health coverage. This study aimed to identify key enablers to support policy development for national pharmacy workforce advancement.DesignA cross-sectional country-level questionnaire was distributed from July 2018 to March 2019.SettingNational-level or country-level pharmacy workforce development policy.ParticipantsProfessional leadership associations and national agencies of the International Pharmaceutical Federation (FIP). The FIP global database included 129 countries.MeasuresThe questionnaire was designed to collate data on the scope of advanced and specialist practice in respondent countries. Multiple correspondence analysis and subsequent cluster analysis were conducted to explore the associations and patterns of country-level attributes of systems in place for the pharmacy workforce advancement in order to develop a general transnational model for country-level advanced practice development.ResultsEighty-eight countries (68.2% response rate) responded to the questionnaire. Factors that enhance and contribute to advanced practice policy development include the country’s socioeconomic factors and the availability of national practice advancement concepts. The essential advancement concepts include the availability of framework and professional recognition systems, programmes assisting advanced practice development and workforce advancement and recognition opportunities. Cluster analysis identified three clusters of country respondents. First cluster included low-income and middle-income with poor pharmacy advancement implementation, second cluster included a higher socioeconomic status with weaker pharmacy workforce advancement implementation and third cluster included upper middle-income to high-income countries and high rates of pharmacy advancement implementation.ConclusionThe key factors identified in this study can be used to support a transnational approach to pharmacy workforce advancement. The three clusters identified highlighted that workforce advancement was not an exclusive trait of higher-income countries. Lessons from countries that have already adopted concepts of advancement in pharmacy practice could be adopted to other countries to accelerate the progress of advanced practice globally.
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Pahuja, Anjali K., Kundan Singh Chufal, Irfan Ahmad, Ram Bajpai, Rajpal Singh, Rahul Lal Chowdhary, and Maithili Sharma. "Identifying Prognostic Groups Using Machine Learning Tools in Patients Undergoing Chemoradiation for Inoperable Locally Advanced Nonsmall Cell Lung Carcinoma." Asian Journal of Oncology 05, no. 02 (July 2019): 056–63. http://dx.doi.org/10.1055/s-0039-3401437.

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Abstract Introduction Unresectable stage III nonsmall cell lung cancer (NSCLC) continues to have dismal 5-year overall survival (OS) rate. However, a subset of the patients treated with chemoradiation show significantly better outcome. Prediction of treatment outcome can be improved by utilizing machine learning tools, such as cluster analysis (CA), and is capable of identifying complex interactions among many variables. We have utilized CA to identify a cluster with good prognosis within stage III NSCLC. Materials and Methods Retrospective analysis of treatment outcomes was done for 92 patients who underwent chemoradiation for inoperable locally advanced NSCLC from 2012 to 2018. Using various patient- and treatment-related variables, an exploratory factor analysis was performed to extract factors with eigenvalue > 1. An appropriate number of homogeneous groups were identified using agglomerative hierarchical cluster analysis. Further K-mean cluster analysis was applied to classify each patient into their homogeneous clusters. The newly formed cluster variable was used as an independent variable to estimate survival over time using Kaplan–Meier method. Results With a median follow-up of 18 months, median OS was 14 months. Using CA, three prognostic clusters were obtained. Cluster 2 with 36 patients had a median OS of 36 months, whereas Cluster 1 with 34 patients had a median OS of 20 months (p = 0.004). Conclusion A cluster could thus be identified with a relatively good prognosis within stage III NSCLC. Using CA, we have attempted to create a model which may provide more specific prognostic information in addition to that provided by tumor node metastasis-based models.
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Yennu, Sriram, Janet L. Williams, Gary B. Chisholm, and Eduardo Bruera. "The effects of dexamethasone and placebo on symptom clusters in advanced cancer patients: A preliminary report." Journal of Clinical Oncology 33, no. 29_suppl (October 10, 2015): 187. http://dx.doi.org/10.1200/jco.2015.33.29_suppl.187.

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187 Background: Advanced cancer patients frequently experience debilitating symptoms that occur in clusters, but few pharmacological studies have targeted symptom clusters. Our objective was to examine the effects of dexamethasone on symptom clusters. Methods: Secondary analysis of a recent RCT of dexamethasone (DEX) vs placebo (PL) on cancer symptoms as assessed by FACIT-F-Fatigue; FAACT-Anorexia-Cachexia; BPI - Pain; HADS- Anxiety-Depression; ESAS: Sleep, Drowsiness, Dyspnea. Symptom clusters were identified based on baseline symptoms [ESAS] using principal component analysis. Cluster scores were computed by adding each scale divided by the maximum value for the scale: Fatigue- Anorexia-Depression = (Fatigue /52 + Anorexia/48+ HADS-Depression/21); Sleep-Anxiety-Drowsiness = (Sleep/10+HADS-Anxiety/21+Drowsiness /10); Pain-Dyspnea = (BPI/10 +Dyspnea /10). Higher number indicates better QOL. Correlations and change in the severity of symptom clusters were analyzed. Results: In 114 evaluable patients, 3 clusters accounted for 63% of the total variance at baseline: Fatigue-anorexia/cachexia-depression cluster (FAD); sleep-anxiety-drowsiness cluster (SAD) and Pain-Dyspnea cluster (PD). Median (IQR) improvement in the FAD cluster at Day 15 and Day 8 was significantly higher in the DEX than in the PL group [0.22 (-0.04, 0.45) vs. 0.06 (-.30, .20), P = 0.016)] and [0.15 (-0.84, 0.35) vs-0.095 (-0.35, 0.16), p = 0.017] respectively. There was no significant change observed in SAD and PD after DEX. Median (IQR) scores for FAD and PD of the DEX group at baseline, day 8, and day 15 were 1.42(1.1,1.7),1.71(1.3,2.1),1.78(1.4,2.2); [1.1(0.8,1.4); 1.38(.04,1.6); 1.43(1.3,1.7) respectively and significantly correlated over time at Day 8 (r = 0.76; p < 0.001) Day 15 (r = 0.55;p < 0.001) [FAD]; Day 8 (r = 0.36; p < 0.001) Day 15 (r = 0.45; p < 0.001) [PD]. Conclusions: FAD cluster showed improvement with dexamethasone and consistent correlation overtime, as compared to SAD and PD cluster. These findings suggest that fatigue-anorexia/cachexia- and depression share a common a common pathophysiologic basis. Further studies are needed to investigate this cluster and target anti-inflammatory therapies. Clinical trial information: NCT00489307.
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Dissertations / Theses on the topic "Advanced Cluster Analysis"

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Yiu, Man-lung. "Advanced query processing on spatial networks." Click to view the E-thesis via HKUTO, 2006. http://sunzi.lib.hku.hk/hkuto/record/B36279365.

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Yiu, Man-lung, and 姚文龍. "Advanced query processing on spatial networks." Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 2006. http://hub.hku.hk/bib/B36279365.

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Lärkefjord, Vivianne Cecilia Jennifer. "Rural Development, Equine Businesses in Vaggeryd’s Municipality : Empirical Analysis and Outlook." Thesis, Jönköping University, JIBS, Economics, 2010. http://urn.kb.se/resolve?urn=urn:nbn:se:hj:diva-12013.

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Vaggeryd is a rural municipality and wants to develop its rural areas. Therefore Vaggeryd’s municipality wants to become a cluster region within the equus industries and hence become an equine municipality where equestrians can live and run their businesses as well as providing for equine activities and competitions. The reason why it is interesting to create a cluster region is because it comes with benefits for the companies and it helps developing the regional economy (Enright, 1998; Porter, 2000). “Clusters lower barriers to new business formation that improve the environment for productivity.” (Porter, 1998a). One investment made in 2005 in the direction of becoming an equine municipality was the start up of an Advanced Vocational Education [KY] in the region, named Horse Entrepreneur.

All 23 students in this programme, year 2005 and 2006, were interviewed and it turned out that all of them wanted to start up own businesses within 10 years, but only three planned to establish their companies in Vaggeryd. Most of the students planned to move back to their home municipality. Eight students in total could, however imagine themselves establishing in Vaggeryd. The students thought that this kind of education is very important for equestrians who want to start up businesses.

The students believed that Vaggeryd’s municipality will become a good and functional equine municipality and referred to the highway E4 (which runs through the municipality), the race track and the veterinary clinic. More farms with large lands are what the students think the municipality council should invest in to attract equine companies as well as to become an equine friendly municipality.

From the cluster analysis a similar result was found. It showed that there is no agglomeration in the four investigated industries over Sweden, namely horse breeding, horse competitions, managing race tracks and gallops courses and riding educational services and horse rents. This means that the places where the entrepreneurs choose to set up their businesses are less important; hence they can likewise set up in Vaggeryd.

However a trend was expected. The districts where most of the equine businesses in Sweden take place are rural areas close to populated regions, so called semi-urban areas.

There are no cities in Vaggeryd’s municipality only two towns, Vaggeryd and Skillingaryd. The conclusion drawn is that if Vaggeryd wants to become an equine municipality it must invest (Enright, 1998; Malmberg, 2002). For example creating job opportunities, and building farms on areas with large lands. These investments are needed to attract inhabitants and business people to their municipality before the equine companies can flourish. Investments make this type of clusters attractive (Enright, 1998; Malmberg, 2002).

The cluster data revealed that there are no equine activities in Vaggeryd’s municipality today; however it has the possibility to become an equine municipality due to suitable condition, for example land plots with lakes and the closeness to highway E4.

Vaggeryd should focus on facilitating business activities and providing with updated networks in order for companies to gain from being clustered, according to the cluster theory advocated by von Hippel (1988) and Hakansson (1987). Vaggeryd suits for equine businesses which are trading goods and services with other businesses, hence which are not dependent on a location close to cities, according to Karlsson (2008) in the field of clustering in smaller regions.

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Fang, Zaili. "Some Advanced Model Selection Topics for Nonparametric/Semiparametric Models with High-Dimensional Data." Diss., Virginia Tech, 2012. http://hdl.handle.net/10919/40090.

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Model and variable selection have attracted considerable attention in areas of application where datasets usually contain thousands of variables. Variable selection is a critical step to reduce the dimension of high dimensional data by eliminating irrelevant variables. The general objective of variable selection is not only to obtain a set of cost-effective predictors selected but also to improve prediction and prediction variance. We have made several contributions to this issue through a range of advanced topics: providing a graphical view of Bayesian Variable Selection (BVS), recovering sparsity in multivariate nonparametric models and proposing a testing procedure for evaluating nonlinear interaction effect in a semiparametric model. To address the first topic, we propose a new Bayesian variable selection approach via the graphical model and the Ising model, which we refer to the ``Bayesian Ising Graphical Model'' (BIGM). There are several advantages of our BIGM: it is easy to (1) employ the single-site updating and cluster updating algorithm, both of which are suitable for problems with small sample sizes and a larger number of variables, (2) extend this approach to nonparametric regression models, and (3) incorporate graphical prior information. In the second topic, we propose a Nonnegative Garrote on a Kernel machine (NGK) to recover sparsity of input variables in smoothing functions. We model the smoothing function by a least squares kernel machine and construct a nonnegative garrote on the kernel model as the function of the similarity matrix. An efficient coordinate descent/backfitting algorithm is developed. The third topic involves a specific genetic pathway dataset in which the pathways interact with the environmental variables. We propose a semiparametric method to model the pathway-environment interaction. We then employ a restricted likelihood ratio test and a score test to evaluate the main pathway effect and the pathway-environment interaction.
Ph. D.
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Zhang, Yiqun. "Advances in categorical data clustering." HKBU Institutional Repository, 2019. https://repository.hkbu.edu.hk/etd_oa/658.

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Categorical data are common in various research areas, and clustering is a prevalent technique used for analyse them. However, two challenging problems are encountered in categorical data clustering analysis. The first is that most categorical data distance metrics were actually proposed for nominal data (i.e., a categorical data set that comprises only nominal attributes), ignoring the fact that ordinal attributes are also common in various categorical data sets. As a result, these nominal data distance metrics cannot account for the order information of ordinal attributes and may thus inappropriately measure the distances for ordinal data (i.e., a categorical data set that comprises only ordinal attributes) and mixed categorical data (i.e., a categorical data set that comprises both ordinal and nominal attributes). The second problem is that most hierarchical clustering approaches were actually designed for numerical data and have very high computation costs; that is, with time complexity O(N2) for a data set with N data objects. These issues have presented huge obstacles to the clustering analysis of categorical data. To address the ordinal data distance measurement problem, we studied the characteristics of ordered possible values (also called 'categories' interchangeably in this thesis) of ordinal attributes and propose a novel ordinal data distance metric, which we call the Entropy-Based Distance Metric (EBDM), to quantify the distances between ordinal categories. The EBDM adopts cumulative entropy as a measure to indicate the amount of information in the ordinal categories and simulates the thinking process of changing one's mind between two ordered choices to quantify the distances according to the amount of information in the ordinal categories. The order relationship and the statistical information of the ordinal categories are both considered by the EBDM for more appropriate distance measurement. Experimental results illustrate the superiority of the proposed EBDM in ordinal data clustering. In addition to designing an ordinal data distance metric, we further propose a unified categorical data distance metric that is suitable for distance measurement of all three types of categorical data (i.e., ordinal data, nominal data, and mixed categorical data). The extended version uniformly defines distances and attribute weights for both ordinal and nominal attributes, by which the distances measured for the two types of attributes of a mixed categorical data can be directly combined to obtain the overall distances between data objects with no information loss. Extensive experiments on all three types of categorical data sets demonstrate the effectiveness of the unified distance metric in clustering analysis of categorical data. To address the hierarchical clustering problem of large-scale categorical data, we propose a fast hierarchical clustering framework called the Growing Multi-layer Topology Training (GMTT). The most significant merit of this framework is its ability to reduce the time complexity of most existing hierarchical clustering frameworks (i.e., O(N2)) to O(N1.5) without sacrificing the quality (i.e., clustering accuracy and hierarchical details) of the constructed hierarchy. According to our design, the GMTT framework is applicable to categorical data clustering simply by adopting a categorical data distance metric. To make the GMTT framework suitable for the processing of streaming categorical data, we also provide an incremental version of GMTT that can dynamically adopt new inputs into the hierarchy via local updating. Theoretical analysis proves that the GMTT frameworks have time complexity O(N1.5). Extensive experiments show the efficacy of the GMTT frameworks and demonstrate that they achieve more competitive categorical data clustering performance by adopting the proposed unified distance metric.
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Kanodia, Juveria. "Structural advances for pattern discovery in multi-relational databases /." Link to online version, 2005. https://ritdml.rit.edu/dspace/handle/1850/978.

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Zoll, Marcel Christian Robert. "A search for solar dark matter with the IceCube neutrino detector : Advances in data treatment and analysis technique." Doctoral thesis, Stockholms universitet, Fysikum, 2016. http://urn.kb.se/resolve?urn=urn:nbn:se:su:diva-125341.

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There is compelling observational evidence for the existence of dark matter in the Universe, including our own Galaxy, which could possibly consist of weakly interacting massive particles (WIMPs) not contained in the standard model (SM) of particle physics. WIMPs may get gravitationally trapped inside heavy celestial bodies of ordinary matter. The Sun is a nearby candidate for such a capture process which is driven by the scattering of WIMPs on its nuclei. Forming an over-density at the Sun's core the WIMPs would self-annihilate yielding energetic neutrinos, which leave the Sun and can be detected in experiments on Earth. The cubic-kilometer sized IceCube neutrino observatory, constructed in the clear glacial ice at the Amundsen-Scott South Pole Station in Antarctica offers an excellent opportunity to search for this striking signal. This thesis is dedicated to the search for these solar dark matter signatures in muon neutrinos from the direction of the Sun. Newly developed techniques based on hit clustering and hit-based vetos allow more accurate reconstruction and identification of events in the detector and thereby a stronger rejection of background. These techniques are also applicable to other IceCube analyses and event filters. In addition, new approaches to the analysis without seasonal cuts lead to improvements in sensitivity especially in the low-energy regime (<=100 GeV), the target of the more densely instrumented DeepCore sub-array. This first analysis of 369 days of data recorded with the completed detector array of 86 strings revealed no significant excess above the expected background of atmospheric neutrinos. This allows us to set strong limits on the annihilation rate of WIMPs in the Sun for the models probed in this analysis. The IceCube limits for the spin-independent WIMP-proton scattering cross-section are the most stringent ones for WIMP masses above 100 GeV.
IceCube
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CHUANG, HUI-WEN, and 莊惠雯. "The Study of Concept Advanced Interpretive Structure Analysis and Remedial Instruction on Time Concepts for Each Cluster of Second Graders." Thesis, 2009. http://ndltd.ncl.edu.tw/handle/37600516865595542321.

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碩士
國立臺中教育大學
數學教育學系在職進修教學碩士學位班
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The purposes of this research is to understand the learning situation of the pupils in time concepts, and to apply the concept advanced interpretive structural modeling (CAISM) to analyze the knowledge structure about these time concepts for second graders. The researcher also adopts the fuzzy cluster analysis to discuss the properties of the knowledge structure among each cluster of pupils. Then, the researcher selects two pupils from each cluster as a sample to receive individualized remedial instruction. After the instruction, the pupils take the post-test. Lastly, the researcher compares the differences from the pre-test and post-test to examine the changes of CAISM graphs. The sample includes 32 second graders. The tool used in this study are “self-designed time concepts test”. The main findings of this research are as follows: 1. The researcher can diagnose the learing for individuals by using the CAISM graph, as the basis of remedial instruction. 2. The fuzzy cluster analysis is helpful for the instructor to proceed remedial instruction among each cluster of pupils. 3. Before and after the remedial instruction, the knowledge structure and the proficiency is shown to exhibit differences. 4. The pupils has less opportunity to read a clock and calendar, and instructor shouled use it much often in class and in their daily life.
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Muhammad, Arshad M. "Advances in clustering based on inter-cluster mapping." Thesis, 2016. http://hdl.handle.net/1959.7/uws:37999.

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Data mining involves searching for certain patterns and facts about the structure of data within large complex datasets. Data mining can reveal valuable and interesting relationships which can improve the operations of business, health and many other disciplines. Extraction of hidden patterns and strategic knowledge from large datasets which are stored electronically, is therefore a challenge faced by many organizations. One commonly used technique in data mining for producing useful results is cluster analysis. A basic issue in cluster analysis is deciding the optimal number of clusters for a dataset. A solution to this issue is not straightforward as this form of clustering is unsupervised learning and no clear definition of cluster quality exists. In addition, this issue will be more challenging and complicated for multi-dimensional datasets. Finding the estimated number of clusters and their quality is generally based on so-called validation indexes. A limitation with typical existing validation indexes is that they only work well with specific types of datasets compatible with their design assumptions. Also their results may be inconsistent and an algorithm may need to be run multiple times to find a best estimate of the number of clusters. Furthermore, these existing approaches may not be effective for complex problems in large datasets with varied structure. To help overcome these deficiencies, an efficient and effective approach for stable estimation of the number of clusters is essential. Many clustering techniques including partitioning, hierarchal, grid-base and model-based clustering are available. Here we consider only the partitioning method e.g. the k-means clustering algorithm for analysing data. This thesis will describe a new approach for stable estimation of the number of clusters, based on use of the k-means clustering algorithm. First results obtained from the k-means clustering algorithm will be used to gain a forward and backward mapping of common elements for adjacent and non-adjacent clusters. These will be represented in the form of proportion matrices which will be used to compute combined mapped information using a matrix inner product similarity measure. This will provide indicators for the similarity of mapped elements and overlap (dissimilarity), average similarity and average overlap (average dissimilarity) between clusters. Finally, the estimated number of clusters will be decided using the maximum average similarity, minimum average overlap and coefficient of variation measure. The new approach provides more information than an application of typical existing validation indexes. For example, the new approach offers not only the estimated number of clusters but also gives an indication of fully or partially separated clusters and defines a set of stable clusters for the estimated number of clusters. The advantage of the new approach over several existing validation indexes for evaluating clustering results is demonstrated empirically by applying it on a variety of simulated and real datasets.
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Books on the topic "Advanced Cluster Analysis"

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ADMA 2008 (2008 Chengdu, China). Advanced data mining and applications: 4th international conference, ADMA 2008, Chengdu, China, October 8-10, 2008, proceedings. New York: Springer, 2008.

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ADMA 2008 (2008 Chengdu, China). Advanced data mining and applications: 4th international conference, ADMA 2008, Chengdu, China, October 8-10, 2008, proceedings. New York: Springer, 2008.

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Changjie, Tang, ed. Advanced data mining and applications: 4th international conference, ADMA 2008, Chengdu, China, October 8-10, 2008, proceedings. New York: Springer, 2008.

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Cao, Longbing. Advanced Data Mining and Applications: 6th International Conference, ADMA 2010, Chongqing, China, November 19-21, 2010, Proceedings, Part II. Berlin, Heidelberg: Springer Berlin Heidelberg, 2010.

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Zhou, Shuigeng. Advanced Data Mining and Applications: 8th International Conference, ADMA 2012, Nanjing, China, December 15-18, 2012. Proceedings. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012.

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International Federation of Classification Societies. Conference. Classification, clustering and data analysis: Recent advances and applications. Berlin: Springer, 2002.

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Sugato, Basu, Davidson Ian 1971-, and Wagstaff Kiri, eds. Constrained clustering: Advances in algorithms, theory, and applications. Boca Raton, FL: Chapman & Hall/CRC, 2008.

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International Federation of Classification Societies. Conference. Advances in data science and classification: Proceedings of the 6th Conference of the International Federation of Classification Societies (IFCS-98), Università "La Sapienza", Rome, 21-24 July, 1998. New York: Springer, 1998.

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Ling, Charles X., Nick Cercone, Xiaofang Zhou, Xue Li, and Changjie Tang. Advanced Data Mining and Applications: 4th International Conference, ADMA 2008, Chengdu, China, October 8-10, 2008, Proceedings. Springer, 2008.

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Wang, Sen, Jianxin Li, Shuliang Wang, Xue Li, and Shaowen Qin. Advanced Data Mining and Applications: 15th International Conference, ADMA 2019, Dalian, China, November 21–23, 2019, Proceedings. Springer, 2019.

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Book chapters on the topic "Advanced Cluster Analysis"

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Aggarwal, Charu C. "Cluster Analysis: Advanced Concepts." In Data Mining, 205–36. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-14142-8_7.

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Yokoyama, Satoru. "Improving Algorithm for Overlapping Cluster Analysis." In Advanced Studies in Behaviormetrics and Data Science, 329–38. Singapore: Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-15-2700-5_20.

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Zhang, Ke-Bing, Mehmet A. Orgun, and Kang Zhang. "HOV3: An Approach to Visual Cluster Analysis." In Advanced Data Mining and Applications, 316–27. Berlin, Heidelberg: Springer Berlin Heidelberg, 2006. http://dx.doi.org/10.1007/11811305_35.

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Ettetuani, Boutaina, Ahmed Moussa, and Rajaa Chahboune. "Functional Cluster Analysis of Glomerular Disease." In Advanced Intelligent Systems for Sustainable Development (AI2SD’2020), 1116–23. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-90633-7_99.

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Ah-Pine, Julien. "Cluster Analysis Based on the Central Tendency Deviation Principle." In Advanced Data Mining and Applications, 5–18. Berlin, Heidelberg: Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-642-03348-3_5.

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El-Shorbagy, M. A., A. Y. Ayoub, I. M. El-Desoky, and A. A. Mousa. "A Novel Genetic Algorithm Based k-means Algorithm for Cluster Analysis." In The International Conference on Advanced Machine Learning Technologies and Applications (AMLTA2018), 92–101. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-74690-6_10.

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Chang, Te-Min, Sin-Jin Lin, and Ming-Fu Hsu. "An integrated data transformation, cluster determination, and data envelopment analysis for advanced decision making." In System Innovation for a World in Transition, 238–41. London: CRC Press, 2023. http://dx.doi.org/10.1201/9781003460763-49.

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Taşdemir, Kadim, and Erzsébet Merényi. "Cluster Analysis in Remote Sensing Spectral Imagery through Graph Representation and Advanced SOM Visualization." In Discovery Science, 259–71. Berlin, Heidelberg: Springer Berlin Heidelberg, 2008. http://dx.doi.org/10.1007/978-3-540-88411-8_25.

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Chiu, Po-Cheich, Yar-Sun Hsu, and Ching-Te Chiu. "Analysis of Cluster Ring Controller/Area Networks for Enhanced Transmission and Fault-Tolerance in Vehicle Networks." In Advanced Microsystems for Automotive Applications 2014, 101–10. Cham: Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-08087-1_10.

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Jankulovska, Mirjana, Sonja Ivanovska, Ljupcho Jankuloski, Mile Markoski, Biljana Kuzmanovska, and Dane Boshev. "Evaluation of advanced wheat mutant lines for food and feed quality." In Mutation breeding, genetic diversity and crop adaptation to climate change, 209–19. Wallingford: CABI, 2021. http://dx.doi.org/10.1079/9781789249095.0021.

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Abstract The main goals of this study were to evaluate the agronomic performance of wheat mutant lines; to detect the effect of genotype, location and different fertilizer levels on analysed traits; to assess seed and feed quality; and to select best performing mutant lines for dual-purpose growing. Ten wheat mutant lines were sown on two locations in Macedonia, for evaluation of their agronomic performance. At both locations, grain yield, straw mass, harvest index, nitrogen use efficiency, nitrogen and protein content in seed and straw, neutral detergent fibre and acid detergent fibre in the straw were determined. In order to classify the genotypes based on all analysed traits, two-way cluster analysis was applied. According to their overall performance, at both locations and with the three different fertilization treatments, the mutant lines were classified in two main groups. The first cluster consisted of mutants 5/1-8, 2/2-21, 4/2-56 and 2/1-51, characterized by very high values for seed yield, straw yield and harvest index, and high to moderate values for all other traits. Only 4/2-56 had very low values for N and protein content in the seed. One mutant line, 6/2-2, did not belong to any of the groups and differed from all other genotypes based on its very low seed and straw yield and very high values for nitrogen and protein content in the straw and neutral detergent fibre. All other mutants belonged to the second group, with low to moderate yield and moderate to high values for the other traits. Mutant lines with the highest seed and straw yield, as well as the best quality of seed and straw under different management systems, were identified and after additional evaluation will be submitted for official variety registration.
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Conference papers on the topic "Advanced Cluster Analysis"

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Ayriyan, Alexander, Gheorghe Adam, Sanda Adam, Vladimir Korenkov, Alexey Lutsenko, and Valery Mitsyn. "CICC JINR Cluster 2008 Performance Improvement." In XII Advanced Computing and Analysis Techniques in Physics Research. Trieste, Italy: Sissa Medialab, 2009. http://dx.doi.org/10.22323/1.070.0054.

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Shahapure, Ketan Rajshekhar, and Charles Nicholas. "Cluster Quality Analysis Using Silhouette Score." In 2020 IEEE 7th International Conference on Data Science and Advanced Analytics (DSAA). IEEE, 2020. http://dx.doi.org/10.1109/dsaa49011.2020.00096.

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Luo, X. Q. "Parallel computing on a PC cluster." In ADVANCED COMPUTING AND ANALYSIS TECHNIQUES IN PHYSICS RESEARCH: VII International Workshop; ACAT 2000. AIP, 2001. http://dx.doi.org/10.1063/1.1405325.

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Furukawa, Masako, Yoshitomo Yaginuma, and Tsuneo Yamada. "Cluster Analysis of Learners Based on Video Annotations." In 2018 7th International Congress on Advanced Applied Informatics (IIAI-AAI). IEEE, 2018. http://dx.doi.org/10.1109/iiai-aai.2018.00193.

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Javadi, Bahman, Mohammad K. Akbari, Jemal H. Abawajy, and Saeid Nahavandi. "Multi-Cluster Computing Interconnection Network Performance Modeling and Analysis." In 2006 International Conference on Advanced Computing and Communications. IEEE, 2006. http://dx.doi.org/10.1109/adcom.2006.4289862.

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Annis, James. "Large scale cluster surveys and distributed computing." In ADVANCED COMPUTING AND ANALYSIS TECHNIQUES IN PHYSICS RESEARCH: VII International Workshop; ACAT 2000. AIP, 2001. http://dx.doi.org/10.1063/1.1405339.

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Cai, Kunpeng, and Maria Rosario Rodavia. "K-Means Cluster Analysis Based on Consumer Behavior." In 2022 4th International Conference on Artificial Intelligence and Advanced Manufacturing (AIAM). IEEE, 2022. http://dx.doi.org/10.1109/aiam57466.2022.00034.

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Flanagan, Kieran, Enda Fallon, Abir Awad, and Paul Connolly. "Self-configuring NetFlow anomaly detection using cluster density analysis." In 2017 19th International Conference on Advanced Communication Technology (ICACT). IEEE, 2017. http://dx.doi.org/10.23919/icact.2017.7890124.

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Wang, Yunxia, Shenghai Qiu, Chengchong Gao, and Jun Wei. "Cluster analysis on the service oriented alliance enterprise manufacturing resource." In 2015 International Conference on Advanced Mechatronic Systems (ICAMechS). IEEE, 2015. http://dx.doi.org/10.1109/icamechs.2015.7287118.

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Zhang, Zhaoxi. "Cluster Analysis of Consumer's Behaviors Based on Unsupervised Learning." In AIAM2021: 2021 3rd International Conference on Artificial Intelligence and Advanced Manufacture. New York, NY, USA: ACM, 2021. http://dx.doi.org/10.1145/3495018.3495360.

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Reports on the topic "Advanced Cluster Analysis"

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Bonnett, Michaela, Angela Ladetto, Meaghan Kennedy, Jasmine Fernandez, and Teri Garstka. Network Analysis of a Mobility Ecosystem in Detroit, MI. Orange Sparkle Ball, June 2024. http://dx.doi.org/10.61152/hejw8941https://www.orangesparkleball.com/innovation-library-blog/2024/5/30/sunbelt2024-network-analysis-of-a-mobility-ecosystem-in-detroit-mi.

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Network Analysis of a Mobility Ecosystem in Detroit, MI Background As part of a new initiative from the Global Epicenter of Mobility (GEM), organizations across many sectors in Detroit, MI, and surrounding counties are collaboratively investing in transforming the local legacy mobility industry into an inclusive advanced mobility cluster over the next 3 years. At the start of this initiative, in partnership with the research team at the Detroit Regional Partnership, a social network analysis was conducted to map the relationship between the foundational 24 organizations, the greater coalition, and their extended network to date. The organizations within this initiative were divided into 4 sectors that highlighted key differences in engagement This baseline map and relationship data, as well as key network analysis metrics, will be compared to future data collections over the coming years to track the initiative’s progress. Methods The original coalition (161 organizations) was identified by the local partner organization and data collection proceeded from September-December 2023 through survey completion. One or more representatives of coalition organizations were asked to identify their relationship to other members of the coalition using a 1-5 scale (Frey et al., 2006). Data were analyzed in R, and organization-level metrics, as well as centralized network-wide metrics, were produced for weighted betweenness, degree, and weighted degree centrality, as well as averages of connection strength. Maps were produced using KUMU software. Findings The mobility coalition consisted of 159 nodes and 7412 connections. Of those connections, 3763 (50.77%) had at least a level 1 connection strength, while 2319 (31.29%) had a connection strength of ≧ 3 (an active working relationship). The average connection strength for the network was 2.13. The coalition network was highly interconnected, with a clustering coefficient of 0.70 and a density of 0.59. Nonprofit and foundation organizations made up 47.5-50% of the top quartile by all centrality metrics while only making up 32.1% of the network. Corporate and private organizations made up 42.8% of the network and made up 68.42-82.50% of the bottom quartile across all metrics. The distribution of centrality scores of the corporate and private organizations was significantly lower than those of all other sectors within the network. These results illuminate a network that is highly interconnected, but in which not all sectors are engaging equally. These results are being used to plan and implement strategic interventions to foster new relationships and growth within the network. In addition to the 159 coalition organizations, respondents to the survey identified an additional 244 organizations as active participants within the Detroit region mobility space. A select number of these organizations will be added to the coalition as it becomes established within the Detroit region. These provide directions for future growth of the GEM initiative and the mobility ecosystem network and are examples of turning research into action.
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Bonnett, Michaela, Angela Ladetto, Meaghan Kennedy, Jasmine Fernandez, and Teri Garstka. Network Analysis of a Mobility Ecosystem in Detroit, MI. Orange Sparkle Ball, June 2024. http://dx.doi.org/10.61152/hejw8941.

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Network Analysis of a Mobility Ecosystem in Detroit, MI Background As part of a new initiative from the Global Epicenter of Mobility (GEM), organizations across many sectors in Detroit, MI, and surrounding counties are collaboratively investing in transforming the local legacy mobility industry into an inclusive advanced mobility cluster over the next 3 years. At the start of this initiative, in partnership with the research team at the Detroit Regional Partnership, a social network analysis was conducted to map the relationship between the foundational 24 organizations, the greater coalition, and their extended network to date. The organizations within this initiative were divided into 4 sectors that highlighted key differences in engagement This baseline map and relationship data, as well as key network analysis metrics, will be compared to future data collections over the coming years to track the initiative’s progress. Methods The original coalition (161 organizations) was identified by the local partner organization and data collection proceeded from September-December 2023 through survey completion. One or more representatives of coalition organizations were asked to identify their relationship to other members of the coalition using a 1-5 scale (Frey et al., 2006). Data were analyzed in R, and organization-level metrics, as well as centralized network-wide metrics, were produced for weighted betweenness, degree, and weighted degree centrality, as well as averages of connection strength. Maps were produced using KUMU software. Findings The mobility coalition consisted of 159 nodes and 7412 connections. Of those connections, 3763 (50.77%) had at least a level 1 connection strength, while 2319 (31.29%) had a connection strength of ≧ 3 (an active working relationship). The average connection strength for the network was 2.13. The coalition network was highly interconnected, with a clustering coefficient of 0.70 and a density of 0.59. Nonprofit and foundation organizations made up 47.5-50% of the top quartile by all centrality metrics while only making up 32.1% of the network. Corporate and private organizations made up 42.8% of the network and made up 68.42-82.50% of the bottom quartile across all metrics. The distribution of centrality scores of the corporate and private organizations was significantly lower than those of all other sectors within the network. These results illuminate a network that is highly interconnected, but in which not all sectors are engaging equally. These results are being used to plan and implement strategic interventions to foster new relationships and growth within the network. In addition to the 159 coalition organizations, respondents to the survey identified an additional 244 organizations as active participants within the Detroit region mobility space. A select number of these organizations will be added to the coalition as it becomes established within the Detroit region. These provide directions for future growth of the GEM initiative and the mobility ecosystem network and are examples of turning research into action.
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Playing the long game: Experimenting Smart Specialisation in the Basque Country 2016-2019. Universidad de Deusto, 2019. http://dx.doi.org/10.18543/ajzo9759.

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Smart specialization strategies (RIS3) represent arguably the most ambitious regional innovation policy ever launched in the EU and as such have posed a major challenge for governments. While developing a smart specialization strategy has not been an entirely new adventure for the Basque Country, which has consistently pursued an industrial strategy over more than thirty years, there is enough novelty in the RIS3 process to pose a challenge even for mature regional innovation policy systems. This report builds on previous analysis of the early implementation of the Basque Country RIS3 (Aranguren et al, 2016) to explore how the processes initially set in motion have subsequently evolved. The focus is on the period 2016-2019 and the analysis is based on interviews with 28 key actors in the Basque RIS3 process alongside a range of other documentary sources. The analysis finds significant changes in the governance of the entrepreneurial discovery processes established in the three strategic priority areas (advanced manufacturing, energy and bio-health) and four opportunity niches (ecosystems, food, urban habitat and creative and cultural industries). These are materializing in changes in the actors engaged and the strategies pursued, and they lead to six core conclusions that might form the basis for recommendations for the future. In line with a ‘living strategy’, a new configuration of priorities is emerging There is an increasing horizontalization taking place, built on cross-cutting concern for internationalization, skills, new business models and entrepreneurship Engaging SMEs remains a huge challenge, and Basque experience points to key roles for cluster associations, local development agencies and vocational training centres The integration of social challenges (and civil society) remains a key challenge, and might take inspiration from Agenda 2030 and from transformative innovation policy or mission-oriented policy approaches There is a specific need for larger, more integrated projects, which will require further adaption of the implementation and policy mix There is need to work on the voice of regions within EU decision-making dynamics and to strengthen coordination across regional initiatives The evolution of the entrepreneurial discovery process observed in the Basque Country sheds light on some of the key issues with the ongoing development of RIS3 across Europe. These include the nature of their experimentalist polity, the further reform of regional research and innovation systems, the rising status of monitoring and evaluation as a strategic diagnostic tool, and the need to re-enforce synergies between EU policy instruments and across EU regions.
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