• Documents
  • Authors
  • Tables
  • Other Seers ▼
    RefSeer AckSeer CollabSeer SeerSeer
  • Log in
  • Sign up
  • MetaCart

CiteSeerX logo

Advanced Search Include Citations
Advanced Search Include Citations | Disambiguate

CHAMELEON: A Hierarchical Clustering Algorithm Using Dynamic Modeling (1999)

Cached

  • Download as a PDF
  •  
  • Download as a PS

Download Links

  • [ftp.cs.umn.edu]
  • [www-users.itlabs.umn.edu]
  • [www.cs.umn.edu]
  • [hercules.ece.utexas.edu]
  • [www.lans.ece.utexas.edu]
  • [www-users.cs.umn.edu]
  • [www.cs.duke.edu]
  • [coblitz.codeen.org:3125]
  • [www2.cs.uh.edu]

  • Save to List
  • Add to Collection
  • Correct Errors
  • Monitor Changes
by George Karypis , Eui-Hong (Sam) Han , Vipin Kumar
Citations:168 - 15 self
  • Summary
  • Active Bibliography
  • Co-citation
  • Clustered Documents
  • Version History

BibTeX

@MISC{Karypis99chameleon:a,
    author = {George Karypis and Eui-Hong (Sam) Han and Vipin Kumar},
    title = {CHAMELEON: A Hierarchical Clustering Algorithm Using Dynamic Modeling},
    year = {1999}
}

Years of Citing Articles

Bookmark

citeulike Connotea Bibsonomy Del.icio.us Digg Reddit

OpenURL

 

Abstract

Clustering in data mining is a discovery process that groups a set of data such that the intracluster similarity is maximized and the intercluster similarity is minimized. Existing clustering algorithms, such as K-means, PAM, CLARANS, DBSCAN, CURE, and ROCK are designed to find clusters that fit some static models. These algorithms can breakdown if the choice of parameters in the static model is incorrect with respect to the data set being clustered, or if the model is not adequate to capture the characteristics of clusters. Furthermore, most of these algorithms breakdown when the data consists of clusters that are of diverse shapes, densities, and sizes. In this paper, we present a novel hierarchical clustering algorithm called CHAMELEON that measures the similarity of two clusters based on a dynamic model. In the clustering process, two clusters are merged only if the inter-connectivity and closeness (proximity) between two clusters are high relative to the internal inter-con...

Citations

1753 Dubes, Algorithms for Clustering Data - Jain, C - 1988
1084 The Design and Analysis of Spatial Data Structures - Samet - 1990
994 Finding Groups in Data: An Introduction to Cluster Analysis - Kaufman, Rousseeuw - 1990
797 A density-based algorithm for discovering clusters in large spatial databases with noise - Ester, Kriegel, et al. - 1996
503 Efficient and effective clustering methods for spatial data mining - Ng, Han - 1994
488 an algorithm for finding best matches in logarithmic expected time - Friedman, Bentley, et al. - 1977
432 CURE: an efficient clustering algorithm for large databases - Guha, Rastogi, et al. - 1998
416 Bayesian classification (AutoClass): Theory and results - Cheeseman, Stutz - 1996
376 Multilevel k-way partitioning scheme for irregular graphs - Karypis, Kumar - 1998
314 Data Mining : An Overview from a Database Perspective - Chen, Han, et al.
262 ROCK: a robust clustering algorithm for categorical attributes - Guha, Rastogi, et al. - 1999
197 Scaling Clustering Algorithms to Large Databases - Bradley, Fayyad, et al. - 1998
177 A cost model for nearest neighbor search in high-dimensional data space - Berchtold, Böhm, et al. - 1997
159 The pyramid-technique: Towards breaking and the curse of dimensionality - Berchtold, Bohm, et al. - 1998
112 The ISPD98 circuit benchmark suite - Alpert - 1998
97 Multilevel k-way Hypergraph Partitioning - Karypis, Kumar
85 Clustering Using a Similarity Measure Based on Shared Nearest Neighbors - Jarvis, Patrick
80 Clustering based on association rule hypergraphs - Han, Karypis, et al. - 1997
71 Document categorization and query generation on the world wide web using WebACE. AI Review (accepted for publication - Boley, Gini, et al. - 1999
58 A fast and highly quality multilevel scheme for partitioning irregular graphs - Karypis, Kumar - 1999
56 Partitioning-based clustering for web document categorization. Decision Support Systems (accepted for publication - Boley, Gini, et al. - 1999
55 A distribution-based clustering algorithm for mining in large spatial databases - XU, ESTER, et al. - 1998
45 Hypergraph based clustering in high-dimensional data sets: A summary of results - Han, Karypis, et al. - 1998
41 Nearest-neighbor clutter removal for estimating features in spatial point processes - Byers, Raftery - 1998
41 Agglomerative clustering using the concept of mutual nearest neighborhood - Gowda, Krishna - 1978
40 Clustering large datasets in arbitrary metric spaces - Ganti, Ramakrishnan, et al. - 1999
35 DBMS research at a crossroads: The vienna update - Stonebraker, Agrawal, et al. - 1993
27 METIS 4.0: Unstructured graph partitioning and sparse matrix ordering system - Karypis, Kumar - 1998
20 Birch: an efficient data clustering method for large databases - Zhang, Ramakrishnan, et al. - 1996
19 Mega-classification: Discovering motifs in massive datastreams - Harris, Hunter, et al. - 1992
17 hmetis 1.5: A hypergraph partitioning package - Karypis, Kumar - 1998
17 Soete. Clustering and Classification - Arabie, De - 1996
6 Implementation and testing of an automated EST processing and analysis system - Shoop, Chi, et al. - 1995
3 Arabidopsis thaliana expressed sequence tags: Generation, analysis and dissemination - Newman, Retzel, et al. - 1995
1 Some fundamental concepts and sysnthesis procedures for pattern recognition preprocessors - Ball, Hall - 1964
The National Science Foundation
  • About CiteSeerX
  • Submit Documents
  • Privacy Policy
  • Help
  • Data
  • Source
  • Contact Us

Developed at and hosted by The College of Information Sciences and Technology

© 2007-2010 The Pennsylvania State University