Relevant bibliographies by topics / Clustering analysi

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  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
  6. Reports

Academic literature on the topic 'Clustering analysi'

Author: Grafiati
Published: 10 March 2023

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Journal articles on the topic "Clustering analysi"

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Jadhav, Priyanka, and Rasika Patil. "Analysis of Clustering technique." International Journal of Trend in Scientific Research and Development Volume-2, Issue-4 (June 30, 2018): 2422–24. http://dx.doi.org/10.31142/ijtsrd15616.

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Manjunath, Mohith, Yi Zhang, Yeonsung Kim, Steve H. Yeo, Omar Sobh, Nathan Russell, Christian Followell, Colleen Bushell, Umberto Ravaioli, and Jun S. Song. "ClusterEnG: an interactive educational web resource for clustering and visualizing high-dimensional data." PeerJ Computer Science 4 (May 21, 2018): e155. http://dx.doi.org/10.7717/peerj-cs.155.

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Background Clustering is one of the most common techniques in data analysis and seeks to group together data points that are similar in some measure. Although there are many computer programs available for performing clustering, a single web resource that provides several state-of-the-art clustering methods, interactive visualizations and evaluation of clustering results is lacking. Methods ClusterEnG (acronym for Clustering Engine for Genomics) provides a web interface for clustering data and interactive visualizations including 3D views, data selection and zoom features. Eighteen clustering validation measures are also presented to aid the user in selecting a suitable algorithm for their dataset. ClusterEnG also aims at educating the user about the similarities and differences between various clustering algorithms and provides tutorials that demonstrate potential pitfalls of each algorithm. Conclusions The web resource will be particularly useful to scientists who are not conversant with computing but want to understand the structure of their data in an intuitive manner. The validation measures facilitate the process of choosing a suitable clustering algorithm among the available options. ClusterEnG is part of a bigger project called KnowEnG (Knowledge Engine for Genomics) and is available at http://education.knoweng.org/clustereng.
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Fisher, D. "Iterative Optimization and Simplification of Hierarchical Clusterings." Journal of Artificial Intelligence Research 4 (April 1, 1996): 147–78. http://dx.doi.org/10.1613/jair.276.

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Clustering is often used for discovering structure in data. Clustering systems differ in the objective function used to evaluate clustering quality and the control strategy used to search the space of clusterings. Ideally, the search strategy should consistently construct clusterings of high quality, but be computationally inexpensive as well. In general, we cannot have it both ways, but we can partition the search so that a system inexpensively constructs a `tentative' clustering for initial examination, followed by iterative optimization, which continues to search in background for improved clusterings. Given this motivation, we evaluate an inexpensive strategy for creating initial clusterings, coupled with several control strategies for iterative optimization, each of which repeatedly modifies an initial clustering in search of a better one. One of these methods appears novel as an iterative optimization strategy in clustering contexts. Once a clustering has been constructed it is judged by analysts -- often according to task-specific criteria. Several authors have abstracted these criteria and posited a generic performance task akin to pattern completion, where the error rate over completed patterns is used to `externally' judge clustering utility. Given this performance task, we adapt resampling-based pruning strategies used by supervised learning systems to the task of simplifying hierarchical clusterings, thus promising to ease post-clustering analysis. Finally, we propose a number of objective functions, based on attribute-selection measures for decision-tree induction, that might perform well on the error rate and simplicity dimensions.
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Patel, Khushbu. "Analysis of Various Database Using Clustering Techniques." Global Journal For Research Analysis 3, no. 7 (June 15, 2012): 59–60. http://dx.doi.org/10.15373/22778160/july2014/20.

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Davidson, Ian, and S. S. Ravi. "Making Existing Clusterings Fairer: Algorithms, Complexity Results and Insights." Proceedings of the AAAI Conference on Artificial Intelligence 34, no. 04 (April 3, 2020): 3733–40. http://dx.doi.org/10.1609/aaai.v34i04.5783.

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We explore the area of fairness in clustering from the different perspective of modifying clusterings from existing algorithms to make them fairer whilst retaining their quality. We formulate the minimal cluster modification for fairness (MCMF) problem where the input is a given partitional clustering and the goal is to minimally change it so that the clustering is still of good quality and fairer. We show using an intricate case analysis that for a single protected variable, the problem is efficiently solvable (i.e., in the class P) by proving that the constraint matrix for an integer linear programming (ILP) formulation is totally unimodular (TU). Interestingly, we show that even for a single protected variable, the addition of simple pairwise guidance (to say ensure individual level fairness) makes the MCMF problem computationally intractable (i.e., NP-hard). Experimental results on Twitter, Census and NYT data sets show that our methods can modify existing clusterings for data sets in excess of 100,000 instances within minutes on laptops and find as fair but higher quality clusterings than fair by design clustering algorithms.
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VEGA-PONS, SANDRO, and JOSÉ RUIZ-SHULCLOPER. "A SURVEY OF CLUSTERING ENSEMBLE ALGORITHMS." International Journal of Pattern Recognition and Artificial Intelligence 25, no. 03 (May 2011): 337–72. http://dx.doi.org/10.1142/s0218001411008683.

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Cluster ensemble has proved to be a good alternative when facing cluster analysis problems. It consists of generating a set of clusterings from the same dataset and combining them into a final clustering. The goal of this combination process is to improve the quality of individual data clusterings. Due to the increasing appearance of new methods, their promising results and the great number of applications, we consider that it is necessary to make a critical analysis of the existing techniques and future projections. This paper presents an overview of clustering ensemble methods that can be very useful for the community of clustering practitioners. The characteristics of several methods are discussed, which may help in the selection of the most appropriate one to solve a problem at hand. We also present a taxonomy of these techniques and illustrate some important applications.
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Madhuri, K., and Mr K. Srinivasa Rao. "Social Media Analysis using Optimized K-Means Clustering." International Journal of Trend in Scientific Research and Development Volume-3, Issue-2 (February 28, 2019): 953–57. http://dx.doi.org/10.31142/ijtsrd21558.

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Li, Hong-Dong, Yunpei Xu, Xiaoshu Zhu, Quan Liu, Gilbert S. Omenn, and Jianxin Wang. "ClusterMine: A knowledge-integrated clustering approach based on expression profiles of gene sets." Journal of Bioinformatics and Computational Biology 18, no. 03 (June 2020): 2040009. http://dx.doi.org/10.1142/s0219720020400090.

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Clustering analysis of gene expression data is essential for understanding complex biological data, and is widely used in important biological applications such as the identification of cell subpopulations and disease subtypes. In commonly used methods such as hierarchical clustering (HC) and consensus clustering (CC), holistic expression profiles of all genes are often used to assess the similarity between samples for clustering. While these methods have been proven successful in identifying sample clusters in many areas, they do not provide information about which gene sets (functions) contribute most to the clustering, thus limiting the interpretability of the resulting cluster. We hypothesize that integrating prior knowledge of annotated gene sets would not only achieve satisfactory clustering performance but also, more importantly, enable potential biological interpretation of clusters. Here we report ClusterMine, an approach that identifies clusters by assessing functional similarity between samples through integrating known annotated gene sets in functional annotation databases such as Gene Ontology. In addition to the cluster membership of each sample as provided by conventional approaches, it also outputs gene sets that most likely contribute to the clustering, thus facilitating biological interpretation. We compare ClusterMine with conventional approaches on nine real-world experimental datasets that represent different application scenarios in biology. We find that ClusterMine achieves better performances and that the gene sets prioritized by our method are biologically meaningful. ClusterMine is implemented as an R package and is freely available at: www.genemine.org/clustermine.php
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Wang, Xing, Jun Wang, Carlotta Domeniconi, Guoxian Yu, Guoqiang Xiao, and Maozu Guo. "Multiple Independent Subspace Clusterings." Proceedings of the AAAI Conference on Artificial Intelligence 33 (July 17, 2019): 5353–60. http://dx.doi.org/10.1609/aaai.v33i01.33015353.

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Multiple clustering aims at discovering diverse ways of organizing data into clusters. Despite the progress made, it’s still a challenge for users to analyze and understand the distinctive structure of each output clustering. To ease this process, we consider diverse clusterings embedded in different subspaces, and analyze the embedding subspaces to shed light into the structure of each clustering. To this end, we provide a two-stage approach called MISC (Multiple Independent Subspace Clusterings). In the first stage, MISC uses independent subspace analysis to seek multiple and statistical independent (i.e. non-redundant) subspaces, and determines the number of subspaces via the minimum description length principle. In the second stage, to account for the intrinsic geometric structure of samples embedded in each subspace, MISC performs graph regularized semi-nonnegative matrix factorization to explore clusters. It additionally integrates the kernel trick into matrix factorization to handle non-linearly separable clusters. Experimental results on synthetic datasets show that MISC can find different interesting clusterings from the sought independent subspaces, and it also outperforms other related and competitive approaches on real-world datasets.
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Kerdprasop, Nittaya, Kacha Chansilp, and Kittisak Kerdprasop. "Greenness Pattern Analysis with the Remote Sensing Index Clustering." International Journal of Machine Learning and Computing 7, no. 6 (December 2017): 181–86. http://dx.doi.org/10.18178/ijmlc.2017.7.6.643.

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Dissertations / Theses on the topic "Clustering analysi"

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Zreik, Rawya. "Analyse statistique des réseaux et applications aux sciences humaines." Thesis, Paris 1, 2016. http://www.theses.fr/2016PA01E061/document.

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Depuis les travaux précurseurs de Moreno (1934), l’analyse des réseaux est devenue une discipline forte, qui ne se limite plus à la sociologie et qui est à présent appliquée à des domaines très variés tels que la biologie, la géographie ou l’histoire. L’intérêt croissant pour l’analyse des réseaux s’explique d’une part par la forte présence de ce type de données dans le monde numérique d’aujourd’hui et, d’autre part, par les progrès récents dans la modélisation et le traitement de ces données. En effet, informaticiens et statisticiens ont porté leurs efforts depuis plus d’une dizaine d’années sur ces données de type réseau en proposant des nombreuses techniques permettant leur analyse. Parmi ces techniques on note les méthodes de clustering qui permettent en particulier de découvrir une structure en groupes cachés dans le réseau. De nombreux facteurs peuvent exercer une influence sur la structure d’un réseau ou rendre les analyses plus faciles à comprendre. Parmi ceux-ci, on trouve deux facteurs importants: le facteur du temps, et le contexte du réseau. Le premier implique l’évolution des connexions entre les nœuds au cours du temps. Le contexte du réseau peut alors être caractérisé par différents types d’informations, par exemple des messages texte (courrier électronique, tweets, Facebook, messages, etc.) échangés entre des nœuds, des informations catégoriques sur les nœuds (âge, sexe, passe-temps, Les fréquences d’interaction (par exemple, le nombre de courriels envoyés ou les commentaires affichés), et ainsi de suite. La prise en considération de ces facteurs nous permet de capturer de plus en plus d’informations complexes et cachées à partir des données. L’objectif de ma thèse été de définir des nouveaux modèles de graphes aléatoires qui prennent en compte les deux facteurs mentionnés ci-dessus, afin de développer l’analyse de la structure du réseau et permettre l’extraction de l’information cachée à partir des données. Ces modèles visent à regrouper les sommets d’un réseau en fonction de leurs profils de connexion et structures de réseau, qui sont statiques ou évoluant dynamiquement au cours du temps. Le point de départ de ces travaux est le modèle de bloc stochastique (SBM). Il s’agit d’un modèle de mélange pour les graphiques qui ont été initialement développés en sciences sociales. Il suppose que les sommets d’un réseau sont répartis sur différentes classes, de sorte que la probabilité d’une arête entre deux sommets ne dépend que des classes auxquelles ils appartiennent
Over the last two decades, network structure analysis has experienced rapid growth with its construction and its intervention in many fields, such as: communication networks, financial transaction networks, gene regulatory networks, disease transmission networks, mobile telephone networks. Social networks are now commonly used to represent the interactions between groups of people; for instance, ourselves, our professional colleagues, our friends and family, are often part of online networks, such as Facebook, Twitter, email. In a network, many factors can exert influence or make analyses easier to understand. Among these, we find two important ones: the time factor, and the network context. The former involves the evolution of connections between nodes over time. The network context can then be characterized by different types of information such as text messages (email, tweets, Facebook, posts, etc.) exchanged between nodes, categorical information on the nodes (age, gender, hobbies, status, etc.), interaction frequencies (e.g., number of emails sent or comments posted), and so on. Taking into consideration these factors can lead to the capture of increasingly complex and hidden information from the data. The aim of this thesis is to define new models for graphs which take into consideration the two factors mentioned above, in order to develop the analysis of network structure and allow extraction of the hidden information from the data. These models aim at clustering the vertices of a network depending on their connection profiles and network structures, which are either static or dynamically evolving. The starting point of this work is the stochastic block model, or SBM. This is a mixture model for graphs which was originally developed in social sciences. It assumes that the vertices of a network are spread over different classes, so that the probability of an edge between two vertices only depends on the classes they belong to
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Karim, Ehsanul, Sri Phani Venkata Siva Krishna Madani, and Feng Yun. "Fuzzy Clustering Analysis." Thesis, Blekinge Tekniska Högskola, Sektionen för ingenjörsvetenskap, 2010. http://urn.kb.se/resolve?urn=urn:nbn:se:bth-2165.

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The Objective of this thesis is to talk about the usage of Fuzzy Logic in pattern recognition. There are different fuzzy approaches to recognize the pattern and the structure in data. The fuzzy approach that we choose to process the data is completely depends on the type of data. Pattern reorganization as we know involves various mathematical transforms so as to render the pattern or structure with the desired properties such as the identification of a probabilistic model which provides the explaination of the process generating the data clarity seen and so on and so forth. With this basic school of thought we plunge into the world of Fuzzy Logic for the process of pattern recognition. Fuzzy Logic like any other mathematical field has its own set of principles, types, representations, usage so on and so forth. Hence our job primarily would focus to venture the ways in which Fuzzy Logic is applied to pattern recognition and knowledge of the results. That is what will be said in topics to follow. Pattern recognition is the collection of all approaches that understand, represent and process the data as segments and features by using fuzzy sets. The representation and processing depend on the selected fuzzy technique and on the problem to be solved. In the broadest sense, pattern recognition is any form of information processing for which both the input and output are different kind of data, medical records, aerial photos, market trends, library catalogs, galactic positions, fingerprints, psychological profiles, cash flows, chemical constituents, demographic features, stock options, military decisions.. Most pattern recognition techniques involve treating the data as a variable and applying standard processing techniques to it.
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Al-Razgan, Muna Saleh. "Weighted clustering ensembles." Fairfax, VA : George Mason University, 2008. http://hdl.handle.net/1920/3212.

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Thesis (Ph.D.)--George Mason University, 2008.
Vita: p. 134. Thesis director: Carlotta Domeniconi. Submitted in partial fulfillment of the requirements for the degree of Doctor of Philosophy in Information Technology. Title from PDF t.p. (viewed Oct. 14, 2008). Includes bibliographical references (p. 128-133). Also issued in print.
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Leisch, Friedrich. "Bagged clustering." SFB Adaptive Information Systems and Modelling in Economics and Management Science, WU Vienna University of Economics and Business, 1999. http://epub.wu.ac.at/1272/1/document.pdf.

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A new ensemble method for cluster analysis is introduced, which can be interpreted in two different ways: As complexity-reducing preprocessing stage for hierarchical clustering and as combination procedure for several partitioning results. The basic idea is to locate and combine structurally stable cluster centers and/or prototypes. Random effects of the training set are reduced by repeatedly training on resampled sets (bootstrap samples). We discuss the algorithm both from a more theoretical and an applied point of view and demonstrate it on several data sets. (author's abstract)
Series: Working Papers SFB "Adaptive Information Systems and Modelling in Economics and Management Science"
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Gupta, Pramod. "Robust clustering algorithms." Thesis, Georgia Institute of Technology, 2011. http://hdl.handle.net/1853/39553.

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One of the most widely used techniques for data clustering is agglomerative clustering. Such algorithms have been long used across any different fields ranging from computational biology to social sciences to computer vision in part because they are simple and their output is easy to interpret. However, many of these algorithms lack any performance guarantees when the data is noisy, incomplete or has outliers, which is the case for most real world data. It is well known that standard linkage algorithms perform extremely poorly in presence of noise. In this work we propose two new robust algorithms for bottom-up agglomerative clustering and give formal theoretical guarantees for their robustness. We show that our algorithms can be used to cluster accurately in cases where the data satisfies a number of natural properties and where the traditional agglomerative algorithms fail. We also extend our algorithms to an inductive setting with similar guarantees, in which we randomly choose a small subset of points from a much larger instance space and generate a hierarchy over this sample and then insert the rest of the points to it to generate a hierarchy over the entire instance space. We then do a systematic experimental analysis of various linkage algorithms and compare their performance on a variety of real world data sets and show that our algorithms do much better at handling various forms of noise as compared to other hierarchical algorithms in the presence of noise.
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Xu, Tianbing. "Nonparametric evolutionary clustering." Diss., Online access via UMI:, 2009.

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Shortreed, Susan. "Learning in spectral clustering /." Thesis, Connect to this title online; UW restricted, 2006. http://hdl.handle.net/1773/8977.

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Ptitsyn, Andrey. "New algorithms for EST clustering." Thesis, University of the Western Cape, 2000. http://etd.uwc.ac.za/index.php?module=etd&amp.

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Expressed sequence tag database is a rich and fast growing source of data for gene expression analysis and drug discovery. Clustering of raw EST data is a necessary step for further analysis and one of the most challenging problems of modem computational biology.
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Karimi, Kambiz. "Clustering analysis of residential loads." Kansas State University, 2016. http://hdl.handle.net/2097/32616.

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Master of Science
Department of Electrical and Computer Engineering
Anil Pahwa
Understanding electricity consumer behavior at different times of the year and throughout the day is very import for utilities. Though electricity consumers pay a fixed predetermined amount of money for using electric energy, the market wholesale prices vary hourly during the day. This analysis is intended to see overall behavior of consumers in different seasons of the year and compare them with the market wholesale prices. Specifically, coincidence of peaks in the loads with peak of market wholesale price is analyzed. This analysis used data from 101 homes in Austin, TX, which are gathered and stored by Pecan Street Inc. These data were used to first determine the average seasonal load profiles of all houses. Secondly, the houses were categorized into three clusters based on similarities in the load profiles using k-means clustering method. Finally, the average seasonal profiles of each cluster with the wholesale market prices which was taken from Electric Reliability Council of Texas (ERCOT) were compared. The data obtained for the houses were in 15-min intervals so they were first changed to average hourly profiles. All the data were then used to determine average seasonal profiles for each house in each season (winter, spring, summer and fall). We decided to set three levels of clusters). All houses were then categorized into one of these three clusters using k-means clustering. Similarly electricity prices taken from ERCOT, which were also on 15-min basis, were changed to hourly averages and then to seasonal averages. Through clustering analysis we found that a low percent of the consumers did not change their pattern of electricity usage while the majority of the users changed their electricity usage pattern once from one season to another. This change in usage patterns mostly depends on level of income, type of heating and cooling systems used, and other electric appliances used. Comparing the ERCOT prices with the average seasonal electricity profiles of each cluster we found that winter and spring seasons are critical for utilities and the ERCOT price peaks in the morning while the peak loads occur in the evening. In summer and fall, on the other hand, ERCOT price and load demand peak at almost the same time with one or two hour difference. This analysis can help utilities and other authorities make better electricity usage policies so they could shift some of the load from the time of peak to other times.
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FARMANI, MOHAMMAD REZA. "Clustering analysis using Swarm Intelligence." Doctoral thesis, Università degli Studi di Cagliari, 2016. http://hdl.handle.net/11584/266871.

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This thesis is concerned with the application of the swarm intelligence methods in clustering analysis of datasets. The main objectives of the thesis are ∙ Take the advantage of a novel evolutionary algorithm, called artificial bee colony, to improve the capability of K-means in finding global optimum clusters in nonlinear partitional clustering problems. ∙ Consider partitional clustering as an optimization problem and an improved antbased algorithm, named Opposition-Based API (after the name of Pachycondyla APIcalis ants), to automatic grouping of large unlabeled datasets. ∙ Define partitional clustering as a multiobjective optimization problem. The aim is to obtain well-separated, connected, and compact clusters and for this purpose, two objective functions have been defined based on the concepts of data connectivity and cohesion. These functions are the core of an efficient multiobjective particle swarm optimization algorithm, which has been devised for and applied to automatic grouping of large unlabeled datasets. For that purpose, this thesis is divided is five main parts: ∙ The first part, including Chapter 1, aims at introducing state of the art of swarm intelligence based clustering methods. ∙ The second part, including Chapter 2, consists in clustering analysis with combination of artificial bee colony algorithm and K-means technique. ∙ The third part, including Chapter 3, consists in a presentation of clustering analysis using opposition-based API algorithm. ∙ The fourth part, including Chapter 4, consists in multiobjective clustering analysis using particle swarm optimization. ∙ Finally, the fifth part, including Chapter 5, concludes the thesis and addresses the future directions and the open issues of this research.
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Books on the topic "Clustering analysi"

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Xu, Rui. Clustering. Hoboken, N.J: Wiley, 2009.

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Mirkin, B. G. Mathematical classification and clustering. Dordrecht: Kluwer Academic Publishers, 1996.

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Phipps, Arabie, Hubert Lawrence J. 1944-, and Soete Geert de, eds. Clustering and classification. Singapore: World Scientific, 1996.

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1968-, Abraham Ajith, and Konar Amit, eds. Metaheuristic clustering. Berlin: Springer, 2009.

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Murtagh, Fionn. Multidimensional clustering algorithms. Vienna: Physica-Verlag, 1985.

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Miyamoto, Sadaaki. Algorithms for fuzzy clustering: Methods in c-means clustering with applications. Berlin: Springer, 2008.

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Jajuga, Krzysztof, Andrzej Sokołowski, and Hans-Hermann Bock, eds. Classification, Clustering, and Data Analysis. Berlin, Heidelberg: Springer Berlin Heidelberg, 2002. http://dx.doi.org/10.1007/978-3-642-56181-8.

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Kusiak, Andrew. Clustering analysis: Models and algorithms. [Urbana, Ill.]: College of Commerce and Business Administration, University of Illinois at Urbana-Champaign, 1985.

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C, Dubes Richard, ed. Algorithms for clustering data. Englewood Cliffs, N.J: Prentice Hall, 1988.

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E, Alexander F., and Boyle P, eds. Methods for investigating localized clustering of disease. Lyon, France: International Agency for Research on Cancer, World Health Organization, 1996.

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Book chapters on the topic "Clustering analysi"

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Govaert, Gérard, and Mohamed Nadif. "Cluster Analysis." In Co-Clustering, 1–53. Hoboken, USA: John Wiley & Sons, Inc., 2014. http://dx.doi.org/10.1002/9781118649480.ch1.

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Gaertler, Marco. "Clustering." In Network Analysis, 178–215. Berlin, Heidelberg: Springer Berlin Heidelberg, 2005. http://dx.doi.org/10.1007/978-3-540-31955-9_8.

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Bolshoy, Alexander, Zeev (Vladimir) Volkovich, Valery Kirzhner, and Zeev Barzily. "Mathematical Models for the Analysis of Natural-Language Documents." In Genome Clustering, 23–42. Berlin, Heidelberg: Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-642-12952-0_3.

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Olive, David J. "Clustering." In Robust Multivariate Analysis, 385–91. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-68253-2_13.

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L. Jockers, Matthew, and Rosamond Thalken. "Clustering." In Text Analysis with R, 177–94. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-39643-5_15.

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Windham, Michael P. "Robust Clustering." In Data Analysis, 385–92. Berlin, Heidelberg: Springer Berlin Heidelberg, 2000. http://dx.doi.org/10.1007/978-3-642-58250-9_31.

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Bagirov, Adil M., and Ehsan Mohebi. "Nonsmooth Optimization Based Algorithms in Cluster Analysis." In Partitional Clustering Algorithms, 99–146. Cham: Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-09259-1_4.

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Phillips, Jeff M. "Clustering." In Mathematical Foundations for Data Analysis, 177–205. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-62341-8_8.

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Billard, Lynne, and Edwin Diday. "Symbolic Regression Analysis." In Classification, Clustering, and Data Analysis, 281–88. Berlin, Heidelberg: Springer Berlin Heidelberg, 2002. http://dx.doi.org/10.1007/978-3-642-56181-8_31.

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Batagelj, Vladimir, and Anuška Ferligoj. "Clustering Relational Data." In Data Analysis, 3–15. Berlin, Heidelberg: Springer Berlin Heidelberg, 2000. http://dx.doi.org/10.1007/978-3-642-58250-9_1.

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Conference papers on the topic "Clustering analysi"

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Ramanujachar, Kartik, and Satish Draksharam. "Note on the Use of Principal Component Analysis (PCA) and Clustering for the Analysis of Wafer Level ATPG data." In ISTFA 2006. ASM International, 2006. http://dx.doi.org/10.31399/asm.cp.istfa2006p0219.

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Abstract This article explores the use of principal component analysis (PCA) and hierarchical clustering in the analysis of wafer level automatic test pattern generation (ATPG) failure data. The principle of commonality is extended by utilizing hierarchical clustering to collect die that are more similar to one another in their manner of failure than to others. Similarity is established by PCA of the patterns that the die in a wafer fail. Results demonstrated that PCA analysis and clustering are useful tools for dimensionality reduction and commonality analysis of wafer level ATPG data. The utility of PCA analysis and clustering in the extraction of die for physical failure analysis is also illustrated.
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Eslahchi, Changiz, Mehdi Sadeghi, Hamid Pezeshk, Mehdi Kargar, Hadi Poormohammadi, Theodore E. Simos, George Psihoyios, and Ch Tsitouras. "Haplotyping Problem, A Clustering Approach." In Numerical Analysis and Applied Mathematics. AIP, 2007. http://dx.doi.org/10.1063/1.2790104.

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Afonso, Carlos, Fábio Ferreira, José Exposto, and Ana I. Pereira. "Comparing clustering and partitioning strategies." In NUMERICAL ANALYSIS AND APPLIED MATHEMATICS ICNAAM 2012: International Conference of Numerical Analysis and Applied Mathematics. AIP, 2012. http://dx.doi.org/10.1063/1.4756254.

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Salgado, Paulo, Lio Gonçalves, Getúlio Igrejas, Theodore E. Simos, George Psihoyios, Ch Tsitouras, and Zacharias Anastassi. "Sliding PCA Fuzzy Clustering Algorithm." In NUMERICAL ANALYSIS AND APPLIED MATHEMATICS ICNAAM 2011: International Conference on Numerical Analysis and Applied Mathematics. AIP, 2011. http://dx.doi.org/10.1063/1.3637005.

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Braginsky, Michael, and Valeriy Buryachenko. "Transformation Field Analysis in Clustering Discretization Method in Micromechanics of Random Structure Composites." In ASME 2022 International Mechanical Engineering Congress and Exposition. American Society of Mechanical Engineers, 2022. http://dx.doi.org/10.1115/imece2022-95138.

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Abstract A linear composite medium consisting of a homogeneous matrix containing either the periodic or random set of heterogeneities is considered. One of the first reduced-order model (ROM) Transformation Field Analysis (TFA) by Dvorak for strains is modified in terms of eigenstresses for implementation to clustering-based ROMs (CROMs) initiated by the self-consistent clustering analysis (SCA) by WK Liu. A set of consistency conditions are obtained for both the effective properties and modified eigenstress concentration factors. One dissects that modified TFA represents a unified background of the central clustering-based ROMs comprising the self-consistent clustering analysis and FEM-clustering analysis. The other presents the scheme of estimation of inhomogeneous strain concentration factors in some prescribed clusters analyzed by the SCA. For statistically homogeneous CMs, a localized version of the modified TFA is proposed for clustering of the matrix (virtual coating) in the vicinity of inclusions. Only linear offline stage is considered whereas analysis of nonlinear online stage is beyond the scope of the current presentation.
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Alasti, Aria, Hassan Salarieh, and Rasool Shabani. "Sliding Mode Control of Electromagnetic System Based on Fuzzy Clustering Estimation: An Experimental Study." In ASME 7th Biennial Conference on Engineering Systems Design and Analysis. ASMEDC, 2004. http://dx.doi.org/10.1115/esda2004-58442.

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Using the combination of fuzzy clustering estimation and sliding mode control, a technique for controlling the magnetic levitation (ML) systems is introduced. This technique is applied to an experimental setup of an ML system for investigating the method derived. The system considered, is a symmetric rotor supported by a cantilever load cell beam and excited by only one electromagnet of a 4-pole magnetic bearing setup. After demonstrating the experimental setup instruction and the specifications of its parts, the clustering, and the sliding mode control methods are explained briefly, then the quality of implementing the techniques to the setup is described step by step. Finally, the results of exercising this method to the setup are illustrated, and they show the good performance of this approach for tracking the desired paths.
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Akbas, Esra, and Peixiang Zhao. "Attributed Graph Clustering." In ASONAM '17: Advances in Social Networks Analysis and Mining 2017. New York, NY, USA: ACM, 2017. http://dx.doi.org/10.1145/3110025.3110092.

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Dinu, Liviu P., and Denis Enăchescu. "On clustering Romance languages." In Recent Advances in Stochastic Modeling and Data Analysis. WORLD SCIENTIFIC, 2007. http://dx.doi.org/10.1142/9789812709691_0061.

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Arıcıoğlu, Mustafa Atilla, Muhittin Koraş, and Mustafa Gömleksiz. "Competitiveness Analysis of the Konya Footwear Cluster." In International Conference on Eurasian Economies. Eurasian Economists Association, 2014. http://dx.doi.org/10.36880/c05.01134.

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Since the beginning of 1980's advancements in studies on competitiveness and clustering in global scale have also affected the manufacturing sector in Turkey at the beginning of 2000's. In several debates, it has been strongly emphasized that measurement of both competitiveness and clustering in regional level is a necessity for analyze and implementation processes. This study aims to investigate clustering tendency of footwear manufacturers and to analyze competition power of firms in Konya province in Turkey. For this purpose, footwear industry in the province is analyzed by Porter’s Diamond Model. In addition to Porter’s Model, effect of government factor was included in the analysis. Furthermore, a SWOT analyze was performed through workshops with manufacturers. In regard to competitiveness analysis of footwear industry in Konya, it is shown that the industry has an intermediate competition power. According to analysis, physical conditions of the industry is favorable in context of factor requirements, while lack of human resources is seen as a serious problem in labor-intensive sectors. Also, knowledge spillovers depending upon density and availability in conventional relationships are regarded as an important advantage.
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Hunter, Blake, Thomas Strohmer, Theodore E. Simos, George Psihoyios, and Ch Tsitouras. "Compressive Spectral Clustering." In ICNAAM 2010: International Conference of Numerical Analysis and Applied Mathematics 2010. AIP, 2010. http://dx.doi.org/10.1063/1.3498187.

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Reports on the topic "Clustering analysi"

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Kryzhanivs'kyi, Evstakhii, Liliana Horal, Iryna Perevozova, Vira Shyiko, Nataliia Mykytiuk, and Maria Berlous. Fuzzy cluster analysis of indicators for assessing the potential of recreational forest use. [б. в.], October 2020. http://dx.doi.org/10.31812/123456789/4470.

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Cluster analysis of the efficiency of the recreational forest use of the region by separate components of the recreational forest use potential is provided in the article. The main stages of the cluster analysis of the recreational forest use level based on the predetermined components were determined. Among the agglomerative methods of cluster analysis, intended for grouping and combining the objects of study, it is common to distinguish the three most common types: the hierarchical method or the method of tree clustering; the K-means Clustering Method and the two-step aggregation method. For the correct selection of clusters, a comparative analysis of several methods was performed: arithmetic mean ranks, hierarchical methods followed by dendrogram construction, K- means method, which refers to reference methods, in which the number of groups is specified by the user. The cluster analysis of forestries by twenty analytical grounds was not proved by analysis of variance, so the re-clustering of certain objects was carried out according to the nine most significant analytical features. As a result, the forestry was clustered into four clusters. The conducted cluster analysis with the use of different methods allows us to state that their combination helps to select reasonable groupings, clearly illustrate the clustering procedure and rank the obtained forestry clusters.
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Chen, Maximillian Gene, Kristin Marie Divis, James D. Morrow, and Laura A. McNamara. Visualizing Clustering and Uncertainty Analysis with Multivariate Longitudinal Data. Office of Scientific and Technical Information (OSTI), September 2018. http://dx.doi.org/10.2172/1472228.

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Martone, Anthony, Roberto Innocenti, and Kenneth Ranney. An Analysis of Clustering Tools for Moving Target Indication. Fort Belvoir, VA: Defense Technical Information Center, November 2009. http://dx.doi.org/10.21236/ada512473.

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Kanungo, T., D. M. Mount, N. S. Netanyahu, C. Piatko, R. Silverman, and A. Y. Wu. The Analysis of a Simple k-Means Clustering Algorithm. Fort Belvoir, VA: Defense Technical Information Center, January 2000. http://dx.doi.org/10.21236/ada458738.

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Fraley, Chris, and Adrian E. Raftery. MCLUST: Software for Model-Based Clustering, Density Estimation and Discriminant Analysis. Fort Belvoir, VA: Defense Technical Information Center, October 2002. http://dx.doi.org/10.21236/ada459792.

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Cordeiro de Amorim, Renato. A survey on feature weighting based K-Means algorithms. Web of Open Science, December 2020. http://dx.doi.org/10.37686/ser.v1i2.79.

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In a real-world data set there is always the possibility, rather high in our opinion, that different features may have different degrees of relevance. Most machine learning algorithms deal with this fact by either selecting or deselecting features in the data preprocessing phase. However, we maintain that even among relevant features there may be different degrees of relevance, and this should be taken into account during the clustering process. With over 50 years of history, K-Means is arguably the most popular partitional clustering algorithm there is. The first K-Means based clustering algorithm to compute feature weights was designed just over 30 years ago. Various such algorithms have been designed since but there has not been, to our knowledge, a survey integrating empirical evidence of cluster recovery ability, common flaws, and possible directions for future research. This paper elaborates on the concept of feature weighting and addresses these issues by critically analysing some of the most popular, or innovative, feature weighting mechanisms based in K-Means
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Harris, J. Clustering of gamma ray spectrometer data using a computer image analysis system. Natural Resources Canada/ESS/Scientific and Technical Publishing Services, 1990. http://dx.doi.org/10.4095/128043.

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Choudhary, Alok, Ankit Agrawal, and Wei-Keng Liao. Scalable, In-situ Data Clustering Data Analysis for Extreme Scale Scientific Computing. Office of Scientific and Technical Information (OSTI), July 2021. http://dx.doi.org/10.2172/1896359.

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Hehr, Brian Douglas. LDRD Report : Analysis of Defect Clustering in Semiconductors using Kinetic Monte Carlo Methods. Office of Scientific and Technical Information (OSTI), January 2014. http://dx.doi.org/10.2172/1465520.

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Perr-Sauer, Jordan, Adam W. Duran, and Caleb T. Phillips. Clustering Analysis of Commercial Vehicles Using Automatically Extracted Features from Time Series Data. Office of Scientific and Technical Information (OSTI), January 2020. http://dx.doi.org/10.2172/1597242.

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