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Academic literature on the topic 'Overlapping Clusters'

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Published: 8 September 2023

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Journal articles on the topic "Overlapping Clusters"

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Mirzaie, Mansooreh, Ahmad Barani, Naser Nematbakkhsh, and Majid Mohammad-Beigi. "Bayesian-OverDBC: A Bayesian Density-Based Approach for Modeling Overlapping Clusters." Mathematical Problems in Engineering 2015 (2015): 1–9. http://dx.doi.org/10.1155/2015/187053.

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Although most research in density-based clustering algorithms focused on finding distinct clusters, many real-world applications (such as gene functions in a gene regulatory network) have inherently overlapping clusters. Even with overlapping features, density-based clustering methods do not define a probabilistic model of data. Therefore, it is hard to determine how “good” clustering, predicting, and clustering new data into existing clusters are. Therefore, a probability model for overlap density-based clustering is a critical need for large data analysis. In this paper, a new Bayesian density-based method (Bayesian-OverDBC) for modeling the overlapping clusters is presented. Bayesian-OverDBC can predict the formation of a new cluster. It can also predict the overlapping of cluster with existing clusters. Bayesian-OverDBC has been compared with other algorithms (nonoverlapping and overlapping models). The results show that Bayesian-OverDBC can be significantly better than other methods in analyzing microarray data.
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Singh, Sukhminder. "Estimation in overlapping clusters." Communications in Statistics - Theory and Methods 17, no. 2 (January 1988): 613–21. http://dx.doi.org/10.1080/03610928808829643.

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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.

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<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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Danganan, Alvincent E., and Edjie Malonzo De Los Reyes. "eHMCOKE: an enhanced overlapping clustering algorithm for data analysis." Bulletin of Electrical Engineering and Informatics 10, no. 4 (August 1, 2021): 2212–22. http://dx.doi.org/10.11591/eei.v10i4.2547.

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Improved multi-cluster overlapping k-means extension (IMCOKE) uses median absolute deviation (MAD) in detecting outliers in datasets makes the algorithm more effective with regards to overlapping clustering. Nevertheless, analysis of the applied MAD positioning was not considered. In this paper, the incorporation of MAD used to detect outliers in the datasets was analyzed to determine the appropriate position in identifying the outlier before applying it in the clustering application. And the assumption of the study was the size of the cluster and cluster that are close to each other can led to a higher runtime performance in terms of overlapping clusters. Therefore, additional parameters such as radius of clusters and distance between clusters are added measurements in the algorithm procedures. Evaluation was done through experimentations using synthetic and real datasets. The performance of the eHMCOKE was evaluated via F1-measure criterion, speed and percentage of improvement. Evaluation results revealed that the eHMCOKE takes less time to discover overlap clusters with an improvement rate of 22% and achieved the best performance of 91.5% accuracy rate via F1-measure in identifying overlapping clusters over the IMCOKE algorithm. These results proved that the eHMCOKE significantly outruns the IMCOKE algorithm on mosts of the test conducted.
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Qing, Huan. "Studying Asymmetric Structure in Directed Networks by Overlapping and Non-Overlapping Models." Entropy 24, no. 9 (August 30, 2022): 1216. http://dx.doi.org/10.3390/e24091216.

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We consider the problem of modeling and estimating communities in directed networks. Models to this problem in the previous literature always assume that the sending clusters and the receiving clusters have non-overlapping property or overlapping property simultaneously. However, previous models cannot model the directed network in which nodes in sending clusters have overlapping property, while nodes in receiving clusters have non-overlapping property, especially for the case when the number of sending clusters is no larger than that of the receiving clusters. This kind of directed network exists in the real world for its randomness, and by the fact that we have little prior knowledge of the community structure for some real-world directed networks. To study the asymmetric structure for such directed networks, we propose a flexible and identifiable Overlapping and Non-overlapping model (ONM). We also provide one model as an extension of ONM to model the directed network, with a variation in node degree. Two spectral clustering algorithms are designed to fit the models. We establish a theoretical guarantee on the estimation consistency for the algorithms under the proposed models. A small scale computer-generated directed networks are designed and conducted to support our theoretical results. Four real-world directed networks are used to illustrate the algorithms, and the results reveal the existence of highly mixed nodes and the asymmetric structure for these networks.
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Vidojević, Filip, Dušan Džamić, and Miroslav Marić. "E-function for Fuzzy Clustering in Complex Networks." Ipsi Transactions on Internet research 18, no. 1 (January 1, 2022): 17–21. http://dx.doi.org/10.58245/ipsi.tir.22jr.04.

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In many real-life situations, data consists of entities and the connections between them, which are naturally described by a complex network (graph). The structure of the network is often such that it is possible to group nodes based on the existence of connections between them, where such groups are called clusters (communities, modules). If the nodes are allowed to partially belong to clusters, they are called fuzzy (overlapping) clusters. There is a huge number of algorithms in the literature that perform fuzzy clustering, that is finds overlapping clusters, so a mechanism is needed to evaluate such clustering. The function that assesses the quality of a performed clustering is called the cluster quality function. One of the latest proposed quality functions is the E-function. The E-function is based on a comparison of the internal structure of a cluster, i.e., the connection between nodes within a cluster and the connection of its nodes with the nodes of other clusters. Due to its exponential nature, the E-function is sensitive to small changes in the membership degrees to which the nodes belong to clusters. As such, it has shown good results in evaluating clustering on known data sets. In this paper, the experimental results that the modified E-function achieves in the case of overlapping clusters are presented. Also, some possibilities for fuzzy clustering by optimizing the E-function are displayed.
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Wu, Mary, Byung Chul Ahn, and Chong Gun Kim. "A Channel Reuse Procedure in Clustering Sensor Networks." Applied Mechanics and Materials 284-287 (January 2013): 1981–85. http://dx.doi.org/10.4028/www.scientific.net/amm.284-287.1981.

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Sensor nodes having the limited resource, energy efficiency is an important issue. Clustering on the sensor networks reduces the volume of inter-node communications and raises energy efficiency by transmitting the data collected from members by a cluster head to a sink node. But, due to radio frequency characteristics, interference and collision can occur between neighbor clusters, the resulted re-transmission is more energy consuming. The interference and collision occurred among adjacent clusters can be resolved by assigning non-overlapping channels among neighbor clusters. In this paper, we propose a channel reuse procedure which shows practical steps to assign dynamically channels among adjacent clusters in sensor networks. This method is expected to perform successfully the allocation process of non-overlapping channels for various cluster topologies.
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Alaqtash, Mohammad, Moayad A.Fadhil, and Ali F. Al-Azzawi. "A Modified Overlapping Partitioning Clustering Algorithm for Categorical Data Clustering." Bulletin of Electrical Engineering and Informatics 7, no. 1 (March 1, 2018): 55–62. http://dx.doi.org/10.11591/eei.v7i1.896.

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Clustering is one of the important approaches for Clustering enables the grouping of unlabeled data by partitioning data into clusters with similar patterns. Over the past decades, many clustering algorithms have been developed for various clustering problems. An overlapping partitioning clustering (OPC) algorithm can only handle numerical data. Hence, novel clustering algorithms have been studied extensively to overcome this issue. By increasing the number of objects belonging to one cluster and distance between cluster centers, the study aimed to cluster the textual data type without losing the main functions. The proposed study herein included over twenty newsgroup dataset, which consisted of approximately 20000 textual documents. By introducing some modifications to the traditional algorithm, an acceptable level of homogeneity and completeness of clusters were generated. Modifications were performed on the pre-processing phase and data representation, along with the number methods which influence the primary function of the algorithm. Subsequently, the results were evaluated and compared with the k-means algorithm of the training and test datasets. The results indicated that the modified algorithm could successfully handle the categorical data and produce satisfactory clusters.
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Lee, Kyung-Soon. "Resampling Feedback Documents Using Overlapping Clusters." KIPS Transactions:PartB 16B, no. 3 (June 30, 2009): 247–56. http://dx.doi.org/10.3745/kipstb.2009.16-b.3.247.

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Amdekar, S. J. "An Unbiased Estimator in Overlapping Clusters." Calcutta Statistical Association Bulletin 34, no. 3-4 (September 1985): 231–32. http://dx.doi.org/10.1177/0008068319850312.

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Dissertations / Theses on the topic "Overlapping Clusters"

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Sun, Haojun. "Determining the number of clusters and distinguishing overlapping clusters in data analysis." Thèse, Université de Sherbrooke, 2004. http://savoirs.usherbrooke.ca/handle/11143/5055.

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Le processus de Clustering permet de construire une collection d’objets (clusters) similaires au sein d’un même groupe, et dissimilaires quand ils appartiennent à des groupes différents. Dans cette thèse, on s’intéresse a deux problèmes majeurs d’analyse de données: 1) la détermination automatique du nombre de clusters dans un ensemble de données dont on a aucune information sur les structures qui le composent; 2) le phénomène de recouvrement entre les clusters. La plupart des algorithmes de clustering souffrent du problème de la détermination du nombre de clusters qui est souvent laisse à l’utilisateur. L’approche classique pour déterminer le nombre de clusters est basée sur un processus itératif qui minimise une fonction objectif appelé indice de validité. Notre but est de: 1) développer un nouvel indice de validité pour mesurer la qualité d’une partition, qui est le résultat d’un algorithme de clustering; 2) proposer un nouvel algorithme de clustering flou pour déterminer automatiquement le nombre de clusters. Une application de notre nouvel algorithme est présentée. Elle consiste à la sélection des caractéristiques dans une base de données. Le phénomène de recouvrement entre les clusters est un des problèmes difficile dans la reconnaissance de formes statistiques. La plupart des algorithmes de clustering ont des difficultés à distinguer les clusters qui se chevauchent. Dans cette thèse, on a développé une théorie qui caractérise le phénomène de recouvrement entre les clusters dans un modèle de mélange Gaussien d’une manière formelle. À partir de cette théorie, on a développé un nouvel algorithme qui calcule le degré de recouvrement entre les clusters dans le cas multidimensionnel. Dans ce cadre précis, on a étudié les facteurs qui affectent la valeur théorique du degré de recouvrement. On a démontré comment cette théorie peut être utilisée pour la génération des données de test valides et concrètes pour une évaluation objective des indices de validité pax rapport à leurs capacités à distinguer les clusters qui se chevauchent. Finalement, notre théorie est utilisable dans une application de segmentation des images couleur en utilisant un algorithme de clustering hiérarchique.
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Gesesse, Achamyeleh Dagnaw <1986&gt. "Automatic Extraction of Overlapping Camera Clusters for 3D Reconstruction." Master's Degree Thesis, Università Ca' Foscari Venezia, 2016. http://hdl.handle.net/10579/7516.

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The purpose of this work is to automatically detect overlapping camera clusters for 3-dimensional (3D) reconstruction, using extensions of the dominant set technique. The two driving motives of the thesis were: first, to remove the number of constraints imposed by previous works while running the clustering algorithm, second, to integrate an image selection algorithm in order to enhance the 3D reconstruction performance further. The constraints imposed by previous works have been vanished after we have employed a version of dominant set clustering which allows overlapping. We have also intervened the bulky dense reconstruction phase by an efficient image selection method. The methodology used, for extracting overlapping clusters of cameras, is the dominant sets approach which often converges in a very reasonable time. The replicator dynamics locate individual groups, and after each group extraction the similarity matrix is modified with the aim of destabilizing the located cluster under the dynamics, without affecting the other sets. The entire similarity matrix is always passed to the dynamics; there is no need to cut part of the located group from the graph. Doing so allows an object to be grouped in more than one class, which is our interest. Overlap is important in order to get a smooth (well-covered) reconstruction near cluster boundaries. Experimental results show that the performance of the associated 3D reconstruction is much faster, due to the intervention of image selection algorithm, before the start of a computationally expensive dense reconstruction step. The inputs are list of camera parameters and point clouds found from the famous Bundler - Structure from Motion (SfM) algorithm. Then, our method selects and clusters the cameras, eventually the output is fed to the Patch based Multi View Stereo (PMVS) algorithm. The task of PMVS is producing the final dense reconstruction of the scene. So, in the 3D reconstruction pipeline, our work lies between SfM (which gives sparse 3D point clouds) and PMVS (which gives dense 3D point clouds of the object). Therefore, the outputs of SfM are clustered and selected by our work and then pass to PMVS. In addition to clustering, image selection is employed to cut out unnecessary camera redundancy. Processing near-duplicate images increases the computational time without improving the reconstruction quality. Comparable results, with the current state-of-the-art of overlapping cluster extraction, have been found in this work. The performance of our method is better than the previous work while mantaining the quality precisely the same. We have tested our method on some bench mark camera-datasets and pretty good results are found.
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Dimitrov, Rossen Petkov. "Overlapping of communication and computation and early binding fundamental mechanisms for improving parallel performance on clusters of workstations /." Diss., Mississippi State : Mississippi State University, 2001. http://library.msstate.edu/etd/show.asp?etd=etd-04092001-231941.

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Beka, Sylvia Enobong. "The genomics of Type 1 Diabetes susceptibility regions and effect of regulatory SNPs." Thesis, University of Hertfordshire, 2016. http://hdl.handle.net/2299/17200.

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Human complex diseases, like Diabetes and Cancer, affect many people worldwide today. Despite existing knowledge, many of these diseases are still not preventable. Complex diseases are known to be caused by a combination of genetic factors, as well as environmental and life style factors. The scope of this investigation covered the genomics of Type 1 Diabetes (T1D). There are 49 human genomic regions that are known to carry markers (disease-associated single nucleotide mutations) for T1D, and these were extensively studied in this research. The aim was to find out in how far this disease may be caused by problems in gene regulation rather than in gene coding. For this, the genetic factors associated with T1D, including the single point mutations and susceptibility regions, were characterised on the basis of their genomic attributes. Furthermore, mutations that occur in binding sites for transcription factors were analysed for change in the conspicuousness of their binding region, caused by allele substitution. This is called SNP (Single nucleotide polymorphism) sensitivity. From this study, it was found that the markers for T1D are mostly non-coding SNPs that occur in introns and non-coding gene transcripts, these are structures known to be involved in gene regulatory activity. It was also discovered that the T1D susceptibility regions contain an abundance of intronic, non-coding transcript and regulatory nucleotides, and that they can be split into three distinct groups on the basis of their structural and functional genomic contents. Finally, using an algorithm designed for this study, thirty-seven SNPs that change the representation of their surrounding region were identified. These regulatory mutations are non-associated T1D-SNPs that are mostly characterised by Cytosine to Thymine (C-T) transition mutations. They were found to be closer in average distance to the disease-associated SNPs than other SNPs in binding sites, and also to occur frequently in the binding motifs for the USF (Upstream stimulatory factor) protein family which is linked to problems in Type 2 diabetes.
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Das, Nivedita. "Modeling three-dimensional shape of sand grains using Discrete Element Method." [Tampa, Fla.] : University of South Florida, 2007. http://purl.fcla.edu/usf/dc/et/SFE0002072.

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Tribou, Michael John. "Relative Pose Estimation Using Non-overlapping Multicamera Clusters." Thesis, 2014. http://hdl.handle.net/10012/8141.

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This thesis considers the Simultaneous Localization and Mapping (SLAM) problem using a set of perspective cameras arranged such that there is no overlap in their fields-of-view. With the known and fixed extrinsic calibration of each camera within the cluster, a novel real-time pose estimation system is presented that is able to accurately track the motion of a camera cluster relative to an unknown target object or environment and concurrently generate a model of the structure, using only image-space measurements. A new parameterization for point feature position using a spherical coordinate update is presented which isolates system parameters dependent on global scale, allowing the shape parameters of the system to converge despite the scale parameters remaining uncertain. Furthermore, a flexible initialization scheme is proposed which allows the optimization to converge accurately using only the measurements from the cameras at the first time step. An analysis is presented identifying the configurations of the cluster motions and target structure geometry for which the optimization solution becomes degenerate and the global scale is ambiguous. Results are presented that not only confirm the previously known critical motions for a two-camera cluster, but also provide a complete description of the degeneracies related to the point feature constellations. The proposed algorithms are implemented and verified in experiments with a camera cluster constructed using multiple perspective cameras mounted on a quadrotor vehicle and augmented with tracking markers to collect high-precision ground-truth motion measurements from an optical indoor positioning system. The accuracy and performance of the proposed pose estimation system are confirmed for various motion profiles in both indoor and challenging outdoor environments.
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Huang, Ying-Shuo, and 黃盈碩. "Non-exhaustive Clustering for Overlapping Patent Clusters Analysis." Thesis, 2007. http://ndltd.ncl.edu.tw/handle/15970598844632266402.

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碩士
國立清華大學
工業工程與工程管理學系
95
Facing the challenges from a knowledge-based economy, having a comprehensive understanding insights of technology and industry development is the basic and necessary requirement to gain competitive edges for enterprises. Consequently, enterprises apply technology forecasting techniques to assist generating useful information for further R&D strategic decisions. Nonetheless, current technology forecasting analyses base mostly on macro-indicators such as market share and growth rate rather than on specific technology development information, such as invention and patents of certain technology. In Campbell’s (1983) research, he found that patent documents often better expresses the development trend of technology when compare with ordinary scientific journals. Moreover, according to the report of WIPO (1996), patent documents can better reveal the core technology and innovation than other knowledge documents such as journal papers and technical reports. As a result, we try to incorporate patent analysis while proceed technology forecasting. In this research, a non-exhaustive clustering methodology is proposed as the basis for a novel technology forecasting system. Non-exhaustive clustering methodology allows overlapping of patent documents, which is plausible when any patent can claim multiple key technical inventions. The characteristic of non-exhaustivity emphasizes that one patent contains multiple technology breakthroughs. We use Radio Frequency Identification (RFID) as case example in this research. RFID ontology is constructed. Afterward, refined Normalized Term Frequency/Inverse Document Frequency (NTF-IDF) key-phrase extraction methodology is developed to extract representative key phrases for following clustering procedure. Finally, the non-exhaustive clustering methodology is applied to generate overlapping clusters of patents. The clustering results and analysis of growth trend for each cluster provide users a clear view of patent distribution in a given broad technology area (e.g., RFID). The expected results of this research contain extracting domain key phrases precisely, using the non-exhaustive clustering results as input data of technology forecasting and finally visualize the technology trend. This system enables R&D engineers and managers to find the existing patents related to their interested technical domains (clusters) and enable them to strengthen their R&D efforts offensively and defensively.
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Li, Che-Yu, and 李晢宇. "A Validity Index Method for Clusters with Different Densities and Overlapping." Thesis, 2015. http://ndltd.ncl.edu.tw/handle/gh9877.

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碩士
國立中興大學
資訊科學與工程學系
103
Validity Index is used to estimate the cluster quality, and find the correct number of clusters. In this thesis, we propose a new validity index, which is composed of the spreading measure and overlapping measure of clusters. The spreading measure is used to estimate the degree of dispersion of clusters in a dataset. The overall spreading is the sum of the spreading of all clusters. The overlapping measure is used to estimate the degree of isolation among all clusters. Lower overlapping means large separation between clusters. As a result, a good clustering result is expected to have lower overall spreading and lower overlapping measure. We conducted several experiments to validate the robustness of our validity index, including artificial datasets and public real datasets. Experimental results show that our validity index method has better tolerance for estimating the correct number of clusters with different densities and degrees of overlapping.
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Wu, Cheng-Hshueh, and 吳承學. "An Effective Validity Index Method for Gaussian-distributed Clusters of different sizes with various degrees of Dispersion and Overlapping." Thesis, 2016. http://ndltd.ncl.edu.tw/handle/94560761502870248336.

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碩士
國立中興大學
資訊科學與工程學系
104
Cluster validity index method has two significant functions: assessing the quality of clustering and finding the correct number in cluster grouping. In this thesis, we propose a cluster validity index method, which intends to reduce the problem of a cluster validity index method VDO having little tolerance on estimating correct number of clusters for datasets comprising unbalance-populated clusters. Our new method uses the clustering method siibFCM that can tolerate datasets comprising unbalance-populated clusters along with dispersion and overlapping measures for computing the cluster validity index. The dispersion measure is used to estimate the overall data density of clusters in the dataset. Smaller dispersion means that data points are distributed more closely in all clusters. The overlap measure represents the overall separation between any pair of clusters in the dataset. Low degree of overlap means that clusters are well separated each other. By combining these two metrics, we obtain a good cluster validity index. We conducted several experiments to validate the effectiveness of our validity indexing method, including artificial datasets and public real datasets. Experimental results show that our validity indexing method can effectively and reliably estimate the correct/optimal number of clusters that widely differ in size, dispersion, and overlapping.
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Ho, Ping-Hsuan, and 何秉軒. "A Cluster Validity Indexing Method Based on Entropy for Solving Cluster Overlapping Problem." Thesis, 2014. http://ndltd.ncl.edu.tw/handle/42200508036155950902.

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碩士
國立中興大學
資訊科學與工程學系
102
Data clustering technique can be used in many fields, such as data mining, statistical data analysis, image analysis, pattern recognition and so on. A good clustering can get the benefits of data compression and computational reduction; however, it is unknown about how many clusters that a data set should be partitioned. Having a good guess for an initial number of clusters is highly desired for clustering. The way to find the optimal number of clusters is called cluster validity. In this paper, we propose a new cluster validity indexing method which tries to solve the cluster overlap problem. First, The Fuzzy C-Means algorithm is used to get the necessary information for calculating the validity index. Second, the weight of separation vague on the overlapping part of clusters is increased according to our entropy-based algorithm. Our approach can help increasing the accurate rate of validity index. To demonstrate the effectiveness of our proposed validity index method, we conducted several experiments and compared our method with other cluster validity indices. Experimental results showed that our method is superior to all other methods.
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Books on the topic "Overlapping Clusters"

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Henning, C. Randall. Tangled Governance. Oxford University Press, 2017. http://dx.doi.org/10.1093/oso/9780198801801.001.0001.

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This book addresses the institutions that were deployed to fight the euro crisis, re-establish financial stability, and prevent contagion beyond Europe. It addresses why European leaders chose to include the International Monetary Fund and provides a detailed account of the decisions of the institutions that make up the “troika” (the European Commission, European Central Bank, and IMF). The study explains the institutions’ negotiating strategies, the outcomes of their interaction, and the effectiveness of their cooperation. It also explores the strategies of the member states, including Germany and the United States, with respect to the institutions and the advantages they sought in directing them to work together. The book locates the analysis within the framework of regime complexity, clusters of overlapping and intersecting regional and multilateral institutions. It tests conjectures spawned by that literature against the seven cases of financial rescues of euro-area countries that were stricken by crisis during 2010–15. The book concludes that regime complexity is the consequence of a strategy by key states to control “agency drift.” States mediate conflicts among institutions, through informal as well as formal mechanisms, and thereby limit fragmentation of the regime complex and underpin substantive efficacy. In so doing, the book answers several key puzzles, including why (a) Germany and other northern European countries supported IMF inclusion despite substantive positions opposed to their economic preferences, (b) crisis-fighting arrangements endured intense conflicts among the institutions, and (c) the United States and the IMF promoted further steps to “complete” the monetary union.
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Palfrey, Simon. Formaction. Edited by Henry S. Turner. Oxford University Press, 2017. http://dx.doi.org/10.1093/oxfordhb/9780199641352.013.18.

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This chapter examines formactions—the working parts and craft materials of playworlds—that are often simultaneous, clustered, overlapping, and invisible and do not simply mediate or re-present things in the world, but are themselves vitally immanent with possible life. It argues that ‘theatricality’ describes not a technology of mimesis or even a kind of enacted philosophy, but rather a kind of physics: a world in which bodies, ideas, affects, and figures combine and recombine to generate the plays we watch, read, react to, and think about today. It highlights the value of the category of ‘form’ and uses it to address some of the major methodological problems associated with early modern theatre, including the problem of the ontology of theatre and its creations. It considers the metaphysics of Gottfried Wilhelm Leibniz, with particular emphasis on his philosophy of monads, to think about theatrical life.
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Book chapters on the topic "Overlapping Clusters"

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Khandekar, Rohit, Guy Kortsarz, and Vahab Mirrokni. "Advantage of Overlapping Clusters for Minimizing Conductance." In LATIN 2012: Theoretical Informatics, 494–505. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-29344-3_42.

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Janot, Christian, and Jean-Marie Dubois. "Quasicrystals as Hierarchical Packing of Overlapping Clusters." In Quasicrystals, 183–98. Berlin, Heidelberg: Springer Berlin Heidelberg, 2002. http://dx.doi.org/10.1007/978-3-662-05028-6_8.

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Schwarz, Michael, Simmo Saan, Helmut Seidl, Julian Erhard, and Vesal Vojdani. "Clustered Relational Thread-Modular Abstract Interpretation with Local Traces." In Programming Languages and Systems, 28–58. Cham: Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-30044-8_2.

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AbstractWe construct novel thread-modular analyses that track relational information for potentially overlapping clusters of global variables – given that they are protected by common mutexes. We provide a framework to systematically increase the precision of clustered relational analyses by splitting control locations based on abstractions of local traces. As one instance, we obtain an analysis of dynamic thread creation and joining. Interestingly, tracking less relational information for globals may result in higher precision. We consider the class of 2-decomposable domains that encompasses many weakly relational domains (e.g., Octagons). For these domains, we prove that maximal precision is attained already for clusters of globals of sizes at most 2.
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Torra, Vicenç. "Towards Integrally Private Clustering: Overlapping Clusters for High Privacy Guarantees." In Privacy in Statistical Databases, 62–73. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-13945-1_5.

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Gao, Wei, Kam-Fai Wong, Yunqing Xia, and Ruifeng Xu. "Clique Percolation Method for Finding Naturally Cohesive and Overlapping Document Clusters." In Computer Processing of Oriental Languages. Beyond the Orient: The Research Challenges Ahead, 97–108. Berlin, Heidelberg: Springer Berlin Heidelberg, 2006. http://dx.doi.org/10.1007/11940098_10.

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Tjhi, William-Chandra, and Lihui Chen. "A New Fuzzy Co-clustering Algorithm for Categorization of Datasets with Overlapping Clusters." In Advanced Data Mining and Applications, 328–39. Berlin, Heidelberg: Springer Berlin Heidelberg, 2006. http://dx.doi.org/10.1007/11811305_36.

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Singh, Sarjinder. "Non-Overlapping, Overlapping, Post, and Adaptive Cluster Sampling." In Advanced Sampling Theory with Applications, 765–828. Dordrecht: Springer Netherlands, 2003. http://dx.doi.org/10.1007/978-94-007-0789-4_9.

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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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Yokoyama, Satoru, Atsuho Nakayama, and Akinori Okada. "An Application of One-mode Three-way Overlapping Cluster Analysis." In Studies in Classification, Data Analysis, and Knowledge Organization, 193–200. Berlin, Heidelberg: Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-642-10745-0_20.

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Kim, Paul, and Sangwook Kim. "A Discovery Technique of Overlapping Cluster in Self-Organizing Network." In Convergence and Hybrid Information Technology, 743–48. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-32692-9_94.

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Conference papers on the topic "Overlapping Clusters"

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Das, Sunanda, Shreya Chaudhuri, and Asit K. Das. "Cluster analysis for overlapping clusters using genetic algorithm." In 2016 Second International Conference on Research in Computational Intelligence and Communication Networks (ICRCICN). IEEE, 2016. http://dx.doi.org/10.1109/icrcicn.2016.7813542.

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Štava, Martin, and Pavel Tvrdík. "Overlapping Non-dedicated Clusters Architecture." In 2009 International Conference on Computer Engineering and Technology (ICCET 2009). IEEE, 2009. http://dx.doi.org/10.1109/iccet.2009.66.

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Andersen, Reid, David F. Gleich, and Vahab Mirrokni. "Overlapping clusters for distributed computation." In the fifth ACM international conference. New York, New York, USA: ACM Press, 2012. http://dx.doi.org/10.1145/2124295.2124330.

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Wu, You, Xiong Wang, Zhe Yang, Xiaoying Gan, Xiaohua Tian, and Xinbing Wang. "Crowdclustering items into overlapping clusters." In ICC 2016 - 2016 IEEE International Conference on Communications. IEEE, 2016. http://dx.doi.org/10.1109/icc.2016.7511257.

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Aydin, Nevin, Farid Nait-Abdesselam, Volodymyr Pryyma, and Damla Turgut. "Overlapping Clusters Algorithm in Ad Hoc Networks." In GLOBECOM 2010 - 2010 IEEE Global Communications Conference. IEEE, 2010. http://dx.doi.org/10.1109/glocom.2010.5683454.

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Goldberg, Mark K., Mykola Hayvanovych, and Malik Magdon-Ismail. "Measuring Similarity between Sets of Overlapping Clusters." In 2010 IEEE Second International Conference on Social Computing (SocialCom). IEEE, 2010. http://dx.doi.org/10.1109/socialcom.2010.50.

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He, Xiao, Jing Feng, Bettina Konte, Son T. Mai, and Claudia Plant. "Relevant overlapping subspace clusters on categorical data." In KDD '14: The 20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. New York, NY, USA: ACM, 2014. http://dx.doi.org/10.1145/2623330.2623652.

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Stava, Martin, and Pavel Tvrdik. "Security System for Overlapping Non-dedicated Clusters." In 2009 IEEE International Symposium on Parallel and Distributed Processing with Applications. IEEE, 2009. http://dx.doi.org/10.1109/ispa.2009.19.

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Ripon, Kazi Shah Nawaz, and M. N. H. Siddique. "Evolutionary multi-objective clustering for overlapping clusters detection." In 2009 IEEE Congress on Evolutionary Computation (CEC). IEEE, 2009. http://dx.doi.org/10.1109/cec.2009.4983051.

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Ivannikova, Elena, Anna V. Kononova, and Timo Hamalainen. "Probabilistic group dependence approach for discovering overlapping clusters." In 2016 IEEE 26th International Workshop on Machine Learning for Signal Processing (MLSP). IEEE, 2016. http://dx.doi.org/10.1109/mlsp.2016.7738882.

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