X-means: Extending K-means with Efficient Estimation of the Number of Clusters

Keywords

Estimation, Computer science, Engineering

References

• Multiresolution Instance-Based LearningKan Deng, Andrew Moore — 1995
• The Las Campanas Redshift SurveyStephen A. Shectman, Stephen D. Landy, A. Oemler — 1996
• A Reference Bayesian Test for Nested Hypotheses and its Relationship to the Schwarz CriterionRobert E. Kass, Larry Wasserman — 1995
• The Sloan Digital Sky SurveyAlexander S. Szalay — 1999
• Pattern classification and scene analysisMichael Thompson, Richard O. Duda, Peter E. Hart — 1974
• BIRCH: an efficient data clustering method for very large databasesTian Zhang, Raghu Ramakrishnan, Miron Livny — 1996
• A Database Interface for Clustering in Large Spatial DatabasesMartin Ester, Hans‐Peter Kriegel, Xiaowei Xu — 1995
• Neural Networks for Pattern RecognitionFranziska Wulf — 1993
• Eecient and Eeective Clustering Methods for Spatial Data MiningR. Ng, Jiawei Hany — 1994

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• The X-Alter algorithm : a parameter-free method to perform unsupervised clusteringThomas Laloë, Rémi Servien — 2013
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