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The uniqueness of a good optimum for K-means

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Marina MeilăAuthors Info & Claims

ICML '06: Proceedings of the 23rd international conference on Machine learning

Pages 625 - 632

https://doi.org/10.1145/1143844.1143923

Published: 25 June 2006 Publication History

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Abstract

If we have found a "good" clustering C of a data set, can we prove that C is not far from the (unknown) best clustering C^opt of these data? Perhaps surprisingly, the answer to this question is sometimes yes. When "goodness" is measured by the distortion of K-means clustering, this paper proves spectral bounds on the distance d(C, C^opt). The bounds exist in the case when the data admits a low distortion clustering.

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Index Terms

The uniqueness of a good optimum for K-means
1. Computing methodologies
  1. Machine learning
    1. Learning paradigms
      1. Unsupervised learning
        Cluster analysis

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Published In

ICML '06: Proceedings of the 23rd international conference on Machine learning

June 2006

1154 pages

ISBN:1595933832

DOI:10.1145/1143844

Program Chairs:
William Cohen,
Andrew Moore

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Association for Computing Machinery

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Published: 25 June 2006

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ICML '06 Paper Acceptance Rate 140 of 548 submissions, 26%;

Overall Acceptance Rate 140 of 548 submissions, 26%

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Bhowmick SChoi BBhowmick SChoi B(2023)Pattern Selection for Graph DatabasesPlug-and-Play Visual Subgraph Query Interfaces10.1007/978-3-031-16162-9_6(49-81)Online publication date: 14-Mar-2023
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Hybrid Bisect K-Means Clustering Algorithm

DIC-DOC-K-means: Dissimilarity-based Initial Centroid selection for DOCument clustering using K-means for improving the effectiveness of text document clustering

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