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CARP: Software for Fishing Out Good Clustering Algorithms

Authors:

Volodymyr Melnykov,

Ranjan MaitraAuthors Info & Claims

The Journal of Machine Learning Research, Volume 12

Pages 69 - 73

Published: 01 February 2011 Publication History

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Abstract

This paper presents the CLUSTERING ALGORITHMS' REFEREE PACKAGE or CARP, an open source GNU GPL-licensed C package for evaluating clustering algorithms. Calibrating performance of such algorithms is important and CARP addresses this need by generating datasets of different clustering complexity and by assessing the performance of the concerned algorithm in terms of its ability to classify each dataset relative to the true grouping. This paper briefly describes the software and its capabilities.

References

[1]

L. Hubert and P. Arabie. Comparing partitions. Journal of Classification, 2:193-218, 1985.

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[2]

R. Maitra and V. Melnykov. Simulating data to study performance of finite mixture modeling and clustering algorithms. Journal of Computational and Graphical Statistics, 19(2):354-376, 2010.

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Google Scholar

[3]

W. Qiu and H. Joe. Generation of random clusters with specified degree of separation. Journal of Classification, 23:315-334, 2006.

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D. Steinley and R. Henson. OCLUS: An analytic method for generating clusters with known overlap. Journal of Classification, 22:221-250, 2005.

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Cited By

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Berry NMaitra R(2019)TiK‐meansStatistical Analysis and Data Mining10.1002/sam.1141612:3(223-233)Online publication date: 20-May-2019
https://dl.acm.org/doi/10.1002/sam.11416
Lithio AMaitra R(2018)An efficient k‐means‐type algorithm for clustering datasets with incomplete recordsStatistical Analysis and Data Mining10.1002/sam.1139211:6(296-311)Online publication date: 16-Nov-2018
https://dl.acm.org/doi/10.1002/sam.11392
Riani MCerioli APerrotta DTorti F(2015)Simulating mixtures of multivariate data with fixed cluster overlap in FSDA libraryAdvances in Data Analysis and Classification10.1007/s11634-015-0223-99:4(461-481)Online publication date: 1-Dec-2015
https://dl.acm.org/doi/10.1007/s11634-015-0223-9

Index Terms

CARP: Software for Fishing Out Good Clustering Algorithms
1. Computing methodologies
  1. Machine learning
    1. Learning paradigms
      1. Supervised learning
        Supervised learning by classification
      2. Unsupervised learning
        Cluster analysis
    2. Machine learning approaches
      1. Classification and regression trees

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cover image The Journal of Machine Learning Research

The Journal of Machine Learning Research Volume 12, Issue

2/1/2011

3426 pages

ISSN:1532-4435

EISSN:1533-7928

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JMLR.org

Publication History

Published: 01 February 2011

Published in JMLR Volume 12

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Berry NMaitra R(2019)TiK‐meansStatistical Analysis and Data Mining10.1002/sam.1141612:3(223-233)Online publication date: 20-May-2019
https://dl.acm.org/doi/10.1002/sam.11416
Lithio AMaitra R(2018)An efficient k‐means‐type algorithm for clustering datasets with incomplete recordsStatistical Analysis and Data Mining10.1002/sam.1139211:6(296-311)Online publication date: 16-Nov-2018
https://dl.acm.org/doi/10.1002/sam.11392
Riani MCerioli APerrotta DTorti F(2015)Simulating mixtures of multivariate data with fixed cluster overlap in FSDA libraryAdvances in Data Analysis and Classification10.1007/s11634-015-0223-99:4(461-481)Online publication date: 1-Dec-2015
https://dl.acm.org/doi/10.1007/s11634-015-0223-9

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Abstract

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Cited By

Index Terms

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Adaptive clustering algorithms

Landscape of Clustering Algorithms

K-centers dynamic clustering algorithms and applications

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