Conceptual k-means algorithm with similarity functions

I. O. Ayaquica-Martínez, J. F. Martínez-Trinidad, J. A. Carrasco-Ochoa

Research output: Chapter in Book/Report/Conference proceedingConference contributionResearch

2 Citations (Scopus)

Abstract

The conceptual k-means algorithm consists of two steps. In the first step the clusters are obtained (aggregation step) and in the second one the concepts or properties for those clusters are generated (characterization step). We consider the conceptual k-means management of mixed, qualitative and quantitative, features is inappropriate. Therefore, in this paper, a new conceptual k-means algorithm using similarity functions is proposed. In the aggregation step we propose to use a different clustering strategy, which allows working in a more natural way with object descriptions in terms of quantitative and qualitative features. In addition, an improvement of the characterization step and a new quality measure for the generated concepts are presented. Some results obtained after applying both, the original and the modified algorithms on different databases are shown. Also, they are compared using the proposed quality measure. © Springer-Verlag Berlin Heidelberg 2005.
Original languageAmerican English
Title of host publicationConceptual k-means algorithm with similarity functions
Pages368-376
Number of pages330
ISBN (Electronic)3540298509, 9783540298502
StatePublished - 1 Dec 2005
Externally publishedYes
EventLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) -
Duration: 1 Jan 2014 → …

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume3773 LNCS
ISSN (Print)0302-9743

Conference

ConferenceLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Period1/01/14 → …

Fingerprint

Quality Measures
K-means Algorithm
Aggregation
K-means
Agglomeration
Clustering
Concepts
Similarity
Strategy
Object

Cite this

Ayaquica-Martínez, I. O., Martínez-Trinidad, J. F., & Carrasco-Ochoa, J. A. (2005). Conceptual k-means algorithm with similarity functions. In Conceptual k-means algorithm with similarity functions (pp. 368-376). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 3773 LNCS).
Ayaquica-Martínez, I. O. ; Martínez-Trinidad, J. F. ; Carrasco-Ochoa, J. A. / Conceptual k-means algorithm with similarity functions. Conceptual k-means algorithm with similarity functions. 2005. pp. 368-376 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).
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Ayaquica-Martínez, IO, Martínez-Trinidad, JF & Carrasco-Ochoa, JA 2005, Conceptual k-means algorithm with similarity functions. in Conceptual k-means algorithm with similarity functions. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 3773 LNCS, pp. 368-376, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 1/01/14.

Conceptual k-means algorithm with similarity functions. / Ayaquica-Martínez, I. O.; Martínez-Trinidad, J. F.; Carrasco-Ochoa, J. A.

Conceptual k-means algorithm with similarity functions. 2005. p. 368-376 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 3773 LNCS).

Research output: Chapter in Book/Report/Conference proceedingConference contributionResearch

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Ayaquica-Martínez IO, Martínez-Trinidad JF, Carrasco-Ochoa JA. Conceptual k-means algorithm with similarity functions. In Conceptual k-means algorithm with similarity functions. 2005. p. 368-376. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).