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Survey of clustering algorithms

by Rui Xu, Donald Wunsch II - IEEE TRANSACTIONS ON NEURAL NETWORKS , 2005
"... Data analysis plays an indispensable role for understanding various phenomena. Cluster analysis, primitive exploration with little or no prior knowledge, consists of research developed across a wide variety of communities. The diversity, on one hand, equips us with many tools. On the other hand, the ..."
Abstract - Cited by 499 (4 self) - Add to MetaCart
Data analysis plays an indispensable role for understanding various phenomena. Cluster analysis, primitive exploration with little or no prior knowledge, consists of research developed across a wide variety of communities. The diversity, on one hand, equips us with many tools. On the other hand, the profusion of options causes confusion. We survey clustering algorithms for data sets appearing in statistics, computer science, and machine learning, and illustrate their applications in some benchmark data sets, the traveling salesman problem, and bioinformatics, a new field attracting intensive efforts. Several tightly related topics, proximity measure, and cluster validation, are also discussed.

Survey of clustering data mining techniques

by Pavel Berkhin , 2002
"... Accrue Software, Inc. Clustering is a division of data into groups of similar objects. Representing the data by fewer clusters necessarily loses certain fine details, but achieves simplification. It models data by its clusters. Data modeling puts clustering in a historical perspective rooted in math ..."
Abstract - Cited by 408 (0 self) - Add to MetaCart
Accrue Software, Inc. Clustering is a division of data into groups of similar objects. Representing the data by fewer clusters necessarily loses certain fine details, but achieves simplification. It models data by its clusters. Data modeling puts clustering in a historical perspective rooted in mathematics, statistics, and numerical analysis. From a machine learning perspective clusters correspond to hidden patterns, the search for clusters is unsupervised learning, and the resulting system represents a data concept. From a practical perspective clustering plays an outstanding role in data mining applications such as scientific data exploration, information retrieval and text mining, spatial database applications, Web analysis, CRM, marketing, medical diagnostics, computational biology, and many others. Clustering is the subject of active research in several fields such as statistics, pattern recognition, and machine learning. This survey focuses on clustering in data mining. Data mining adds to clustering the complications of very large datasets with very many attributes of different types. This imposes unique
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Clustering binary data streams with K-means

by Carlos Ordonez - In Proc. ACM SIGMOD Data Mining and Knowledge Discovery Workshop , 2003
"... Clustering data streams is an interesting Data Mining prob-lem. This article presents three variants of the K-means algorithm to cluster binary data streams. The variants in-clude On-line K-means, Scalable K-means, and Incremental K-means, a proposed variant introduced that nds higher quality soluti ..."
Abstract - Cited by 67 (10 self) - Add to MetaCart
Clustering data streams is an interesting Data Mining prob-lem. This article presents three variants of the K-means algorithm to cluster binary data streams. The variants in-clude On-line K-means, Scalable K-means, and Incremental K-means, a proposed variant introduced that nds higher quality solutions in less time. Higher quality of solutions are obtained with a mean-based initialization and incremen-tal learning. The speedup is achieved through a simplied set of sucient statistics and operations with sparse matri-ces. A summary table of clusters is maintained on-line. The K-means variants are compared with respect to quality of results and speed. The proposed algorithms can be used to monitor transactions. 1.
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Similarity Measures for Categorical Data: A Comparative Evaluation

by Shyam Boriah, Varun Chandola, Vipin Kumar , 2008
"... Measuring similarity or distance between two entities is a key step for several data mining and knowledge discovery tasks. The notion of similarity for continuous data is relatively well-understood, but for categorical data, the similarity computation is not straightforward. Several data-driven simi ..."
Abstract - Cited by 57 (3 self) - Add to MetaCart
Measuring similarity or distance between two entities is a key step for several data mining and knowledge discovery tasks. The notion of similarity for continuous data is relatively well-understood, but for categorical data, the similarity computation is not straightforward. Several data-driven similarity measures have been proposed in the literature to compute the similarity between two categorical data instances but their relative performance has not been evaluated. In this paper we study the performance of a variety of similarity measures in the context of a specific data mining task: outlier detection. Results on a variety of data sets show that while no one measure dominates others for all types of problems, some measures are able to have consistently high performance.
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A fuzzy k-modes algorithm for clustering categorical data’, Fuzzy Systems

by Author(s Huang, Z Ng, Zhexue Huang, Michael K. Ng - IEEE Transactions on , 1999
"... ©1999 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other wo ..."
Abstract - Cited by 57 (5 self) - Add to MetaCart
©1999 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.
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Efficient k-anonymization using clustering techniques

by Ji-won Byun, Elisa Bertino, Ninghui Li - In DASFAA , 2007
"... Abstract. k-anonymization techniques have been the focus of intense research in the last few years. An important requirement for such techniques is to ensure anonymization of data while at the same time minimizing the information loss resulting from data modifications. In this paper we propose an ap ..."
Abstract - Cited by 48 (7 self) - Add to MetaCart
Abstract. k-anonymization techniques have been the focus of intense research in the last few years. An important requirement for such techniques is to ensure anonymization of data while at the same time minimizing the information loss resulting from data modifications. In this paper we propose an approach that uses the idea of clustering to minimize information loss and thus ensure good data quality. The key observation here is that data records that are naturally similar to each other should be part of the same equivalence class. We thus formulate a specific clustering problem, referred to as k-member clustering problem. We prove that this problem is NP-hard and present a greedy heuristic, the complexity of which is in O(n 2). As part of our approach we develop a suitable metric to estimate the information loss introduced by generalizations, which works for both numeric and categorical data. 1
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Recent advances in clustering: A brief survey

by S. B. Kotsiantis, P. E. Pintelas - WSEAS Trans. Inform. Sci. Appl
"... Abstract:- Unsupervised learning (clustering) deals with instances, which have not been pre-classified in any way and so do not have a class attribute associated with them. The scope of applying clustering algorithms is to discover useful but unknown classes of items. Unsupervised learning is an app ..."
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Abstract:- Unsupervised learning (clustering) deals with instances, which have not been pre-classified in any way and so do not have a class attribute associated with them. The scope of applying clustering algorithms is to discover useful but unknown classes of items. Unsupervised learning is an approach of learning where instances are automatically placed into meaningful groups based on their similarity. This paper introduces the fundamental concepts of unsupervised learning while it surveys the recent clustering algorithms. Moreover, recent advances in unsupervised learning, such as ensembles of clustering algorithms and distributed clustering, are described.
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Improving the Accuracy and Efficiency of the k-means Clustering Algorithm

by K. A. Abdul Nazeer, M. P. Sebastian
"... Abstract — Emergence of modern techniques for scientific data collection has resulted in large scale accumulation of data pertaining to diverse fields. Conventional database querying methods are inadequate to extract useful information from huge data banks. Cluster analysis is one of the major data ..."
Abstract - Cited by 37 (0 self) - Add to MetaCart
Abstract — Emergence of modern techniques for scientific data collection has resulted in large scale accumulation of data pertaining to diverse fields. Conventional database querying methods are inadequate to extract useful information from huge data banks. Cluster analysis is one of the major data analysis methods and the k-means clustering algorithm is widely used for many practical applications. But the original k-means algorithm is computationally expensive and the quality of the resulting clusters heavily depends on the selection of initial centroids. Several methods have been proposed in the literature for improving the performance of the k-means clustering algorithm. This paper proposes a method for making the algorithm more effective and efficient, so as to get better clustering with reduced complexity.
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Entropy-Based Criterion in Categorical Clustering

by Tao Li, Sheng Ma, Mitsunori Ogihara - Proc. of Intl. Conf. on Machine Learning (ICML , 2004
"... Entropy-type measures for the heterogeneity of clusters have been used for a long time. This paper studies the entropy-based criterion in clustering categorical data. It first shows that the entropy-based criterion can be derived in the formal framework of probabilistic clustering models and e ..."
Abstract - Cited by 35 (4 self) - Add to MetaCart
Entropy-type measures for the heterogeneity of clusters have been used for a long time. This paper studies the entropy-based criterion in clustering categorical data. It first shows that the entropy-based criterion can be derived in the formal framework of probabilistic clustering models and establishes the connection between the criterion and the approach based on dissimilarity coefficients.
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Automated variable weighting in k-means type clustering

by Joshua Zhexue Huang, Michael K. Ng, Hongqiang Rong, Zichen Li - IEEE Transactions on Pattern Analysis and Machine Intelligence , 2005
"... Abstract—This paper proposes a k-means type clustering algorithm that can automatically calculate variable weights. A new step is introduced to the k-means clustering process to iteratively update variable weights based on the current partition of data and a formula for weight calculation is propose ..."
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Abstract—This paper proposes a k-means type clustering algorithm that can automatically calculate variable weights. A new step is introduced to the k-means clustering process to iteratively update variable weights based on the current partition of data and a formula for weight calculation is proposed. The convergency theorem of the new clustering process is given. The variable weights produced by the algorithm measure the importance of variables in clustering and can be used in variable selection in data mining applications where large and complex real data are often involved. Experimental results on both synthetic and real data have shown that the new algorithm outperformed the standard k-means type algorithms in recovering clusters in data. Index Terms—Clustering, data mining, mining methods and algorithms, feature evaluation and selection. 1
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