EPIC: Efficient integration of partitional clustering algorithms for classification

Vikas K. Garg, M. N. Murty

Research output: Chapter in Book/Report/Conference proceedingConference contributionScientificpeer-review

1 Citation (Scopus)


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 languageEnglish
Title of host publicationSimulated Evolution and Learning - 8th International Conference, SEAL 2010, Proceedings
Number of pages5
Publication statusPublished - 2010
MoE publication typeA4 Article in a conference publication
EventInternational Conference on Simulated Evolution and Learning - Kanpur, India
Duration: 1 Dec 20104 Dec 2010
Conference number: 8

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume6457 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


ConferenceInternational Conference on Simulated Evolution and Learning
Abbreviated titleSEAL


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