Results 1 - 10 of 53,357

CURE: An Efficient Clustering Algorithm for Large Data sets

by Sudipto Guha, Rajeev Rastogi, Kyuseok Shim - Published in the Proceedings of the ACM SIGMOD Conference , 1998
"... Clustering, in data mining, is useful for discovering groups and identifying interesting distributions in the underlying data. Traditional clustering algorithms either favor clusters with spherical shapes and similar sizes, or are very fragile in the presence of outliers. We propose a new clustering ..."
Abstract - Cited by 722 (5 self) - Add to MetaCart
Clustering, in data mining, is useful for discovering groups and identifying interesting distributions in the underlying data. Traditional clustering algorithms either favor clusters with spherical shapes and similar sizes, or are very fragile in the presence of outliers. We propose a new

ROCK: A Robust Clustering Algorithm for Categorical Attributes

by Sudipto Guha, Rajeev Rastogi, Kyuseok Shim - In Proc.ofthe15thInt.Conf.onDataEngineering , 2000
"... Clustering, in data mining, is useful to discover distribution patterns in the underlying data. Clustering algorithms usually employ a distance metric based (e.g., euclidean) similarity measure in order to partition the database such that data points in the same partition are more similar than point ..."
Abstract - Cited by 446 (2 self) - Add to MetaCart
points in different partitions. In this paper, we study clustering algorithms for data with boolean and categorical attributes. We show that traditional clustering algorithms that use distances between points for clustering are not appropriate for boolean and categorical attributes. Instead, we propose a

On Spectral Clustering: Analysis and an algorithm

by Andrew Y. Ng, Michael I. Jordan, Yair Weiss - ADVANCES IN NEURAL INFORMATION PROCESSING SYSTEMS , 2001
"... Despite many empirical successes of spectral clustering methods -- algorithms that cluster points using eigenvectors of matrices derived from the distances between the points -- there are several unresolved issues. First, there is a wide variety of algorithms that use the eigenvectors in slightly ..."
Abstract - Cited by 1713 (13 self) - Add to MetaCart
Despite many empirical successes of spectral clustering methods -- algorithms that cluster points using eigenvectors of matrices derived from the distances between the points -- there are several unresolved issues. First, there is a wide variety of algorithms that use the eigenvectors

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
, 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

OPTICS: Ordering Points To Identify the Clustering Structure

by Mihael Ankerst, Markus M. Breunig, Hans-peter Kriegel, Jörg Sander , 1999
"... Cluster analysis is a primary method for database mining. It is either used as a stand-alone tool to get insight into the distribution of a data set, e.g. to focus further analysis and data processing, or as a preprocessing step for other algorithms operating on the detected clusters. Almost all of ..."
Abstract - Cited by 527 (51 self) - Add to MetaCart
Cluster analysis is a primary method for database mining. It is either used as a stand-alone tool to get insight into the distribution of a data set, e.g. to focus further analysis and data processing, or as a preprocessing step for other algorithms operating on the detected clusters. Almost all

Constrained K-means Clustering with Background Knowledge

by Kiri Wagstaff, Claire Cardie, Seth Rogers, Stefan Schroedl - In ICML , 2001
"... Clustering is traditionally viewed as an unsupervised method for data analysis. However, in some cases information about the problem domain is available in addition to the data instances themselves. In this paper, we demonstrate how the popular k-means clustering algorithm can be pro tably modi- ed ..."
Abstract - Cited by 488 (9 self) - Add to MetaCart
Clustering is traditionally viewed as an unsupervised method for data analysis. However, in some cases information about the problem domain is available in addition to the data instances themselves. In this paper, we demonstrate how the popular k-means clustering algorithm can be pro tably modi- ed

Performance comparison of Particle Swarm Optimization with traditional clustering algorithms used in Self Organizing Map

by Anurag Sharma, Christian W. Omlin - International Journal of Computational Intelligence , 2009
"... Abstract—Self-organizing map (SOM) is a well known data reduction technique used in data mining. It can reveal structure in data sets through data visualization that is otherwise hard to detect from raw data alone. However, interpretation through visual inspection is prone to errors and can be very ..."
Abstract - Cited by 1 (0 self) - Add to MetaCart
algorithm utilizes a so-called U-matrix of SOM to determine cluster boundaries; the results of this novel automatic method compare very favorably to boundary detection through traditional algorithms namely k-means and hierarchical based approach which are normally used to interpret the output of SOM

Consistency of spectral clustering

by Ulrike von Luxburg, Mikhail Belkin, Olivier Bousquet , 2004
"... Consistency is a key property of statistical algorithms, when the data is drawn from some underlying probability distribution. Surprisingly, despite decades of work, little is known about consistency of most clustering algorithms. In this paper we investigate consistency of a popular family of spe ..."
Abstract - Cited by 572 (15 self) - Add to MetaCart
Consistency is a key property of statistical algorithms, when the data is drawn from some underlying probability distribution. Surprisingly, despite decades of work, little is known about consistency of most clustering algorithms. In this paper we investigate consistency of a popular family

A density-based algorithm for discovering clusters in large spatial databases with noise

by Martin Ester, Hans-Peter Kriegel, Jörg Sander, Xiaowei Xu , 1996
"... Clustering algorithms are attractive for the task of class identification in spatial databases. However, the application to large spatial databases rises the following requirements for clustering algorithms: minimal requirements of domain knowledge to determine the input parameters, discovery of clu ..."
Abstract - Cited by 1786 (70 self) - Add to MetaCart
Clustering algorithms are attractive for the task of class identification in spatial databases. However, the application to large spatial databases rises the following requirements for clustering algorithms: minimal requirements of domain knowledge to determine the input parameters, discovery

A comparison of document clustering techniques

by Michael Steinbach, George Karypis, Vipin Kumar - In KDD Workshop on Text Mining , 2000
"... This paper presents the results of an experimental study of some common document clustering techniques: agglomerative hierarchical clustering and K-means. (We used both a “standard” K-means algorithm and a “bisecting ” K-means algorithm.) Our results indicate that the bisecting K-means technique is ..."
Abstract - Cited by 613 (27 self) - Add to MetaCart
This paper presents the results of an experimental study of some common document clustering techniques: agglomerative hierarchical clustering and K-means. (We used both a “standard” K-means algorithm and a “bisecting ” K-means algorithm.) Our results indicate that the bisecting K-means technique
Results 1 - 10 of 53,357