EPIC: Efficient integration of partitional clustering algorithms for classification

Vikas K. Garg; M. N. Murty

doi:10.1007/978-3-642-17298-4_79

EPIC: Efficient integration of partitional clustering algorithms for classification

Not published at Aalto University

Research output: Chapter in Book/Report/Conference proceeding › Conference article in proceedings › Scientific › peer-review

1 Citation (Scopus)

Abstract

Partitional algorithms form an extremely popular class of clustering algorithms. Primarily, these algorithms can be classified into two sub-categories: a) k-means based algorithms that presume the knowledge of a suitable k, and b) algorithms such as Leader, which take a distance threshold value, τ, as an input. In this work, we make the following contributions. We 1) propose a novel technique, EPIC, which is based on both the number of clusters, k and the distance threshold, τ, 2) demonstrate that the proposed algorithm achieves better performance than the standard k-means algorithm, and 3) present a generic scheme for integrating EPIC into different classification algorithms to reduce their training time complexity.

Original language	English
Title of host publication	Simulated Evolution and Learning - 8th International Conference, SEAL 2010, Proceedings
Pages	706-710
Number of pages	5
DOIs	https://doi.org/10.1007/978-3-642-17298-4_79
Publication status	Published - 2010
MoE publication type	A4 Conference publication
Event	International Conference on Simulated Evolution and Learning - Kanpur, India Duration: 1 Dec 2010 → 4 Dec 2010 Conference number: 8

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	6457 LNCS
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	International Conference on Simulated Evolution and Learning
Abbreviated title	SEAL
Country/Territory	India
City	Kanpur
Period	01/12/2010 → 04/12/2010

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10.1007/978-3-642-17298-4_79

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Garg, V. K., & Murty, M. N. (2010). EPIC: Efficient integration of partitional clustering algorithms for classification. In Simulated Evolution and Learning - 8th International Conference, SEAL 2010, Proceedings (pp. 706-710). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 6457 LNCS). https://doi.org/10.1007/978-3-642-17298-4_79

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title = "EPIC: Efficient integration of partitional clustering algorithms for classification",

abstract = "Partitional algorithms form an extremely popular class of clustering algorithms. Primarily, these algorithms can be classified into two sub-categories: a) k-means based algorithms that presume the knowledge of a suitable k, and b) algorithms such as Leader, which take a distance threshold value, τ, as an input. In this work, we make the following contributions. We 1) propose a novel technique, EPIC, which is based on both the number of clusters, k and the distance threshold, τ, 2) demonstrate that the proposed algorithm achieves better performance than the standard k-means algorithm, and 3) present a generic scheme for integrating EPIC into different classification algorithms to reduce their training time complexity.",

author = "Garg, {Vikas K.} and Murty, {M. N.}",

year = "2010",

doi = "10.1007/978-3-642-17298-4_79",

language = "English",

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series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",

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note = "International Conference on Simulated Evolution and Learning, SEAL ; Conference date: 01-12-2010 Through 04-12-2010",

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Garg, VK & Murty, MN 2010, EPIC: Efficient integration of partitional clustering algorithms for classification. in Simulated Evolution and Learning - 8th International Conference, SEAL 2010, Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 6457 LNCS, pp. 706-710, International Conference on Simulated Evolution and Learning, Kanpur, India, 01/12/2010. https://doi.org/10.1007/978-3-642-17298-4_79

EPIC: Efficient integration of partitional clustering algorithms for classification. / Garg, Vikas K.; Murty, M. N.
Simulated Evolution and Learning - 8th International Conference, SEAL 2010, Proceedings. 2010. p. 706-710 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 6457 LNCS).

Research output: Chapter in Book/Report/Conference proceeding › Conference article in proceedings › Scientific › peer-review

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N2 - Partitional algorithms form an extremely popular class of clustering algorithms. Primarily, these algorithms can be classified into two sub-categories: a) k-means based algorithms that presume the knowledge of a suitable k, and b) algorithms such as Leader, which take a distance threshold value, τ, as an input. In this work, we make the following contributions. We 1) propose a novel technique, EPIC, which is based on both the number of clusters, k and the distance threshold, τ, 2) demonstrate that the proposed algorithm achieves better performance than the standard k-means algorithm, and 3) present a generic scheme for integrating EPIC into different classification algorithms to reduce their training time complexity.

AB - Partitional algorithms form an extremely popular class of clustering algorithms. Primarily, these algorithms can be classified into two sub-categories: a) k-means based algorithms that presume the knowledge of a suitable k, and b) algorithms such as Leader, which take a distance threshold value, τ, as an input. In this work, we make the following contributions. We 1) propose a novel technique, EPIC, which is based on both the number of clusters, k and the distance threshold, τ, 2) demonstrate that the proposed algorithm achieves better performance than the standard k-means algorithm, and 3) present a generic scheme for integrating EPIC into different classification algorithms to reduce their training time complexity.

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DO - 10.1007/978-3-642-17298-4_79

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SN - 3642172970

SN - 9783642172977

T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

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Garg VK, Murty MN. EPIC: Efficient integration of partitional clustering algorithms for classification. In Simulated Evolution and Learning - 8th International Conference, SEAL 2010, Proceedings. 2010. p. 706-710. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/978-3-642-17298-4_79

EPIC: Efficient integration of partitional clustering algorithms for classification

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