Open AccessFinding number of clusters in single‐step with similarity‐based information‐theoretic algorithm
Author(s) -
Temel T.
Publication year - 2014
Publication title -
electronics letters
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.375
H-Index - 146
eISSN - 1350-911X
pISSN - 0013-5194
DOI - 10.1049/el.2013.3362
Subject(s) - algorithm , cluster (spacecraft) , entropy (arrow of time) , similarity (geometry) , boundary (topology) , computer science , mathematics , function (biology) , sample complexity , data mining , artificial intelligence , mathematical analysis , physics , quantum mechanics , evolutionary biology , image (mathematics) , biology , programming language
A single‐step algorithm is presented to find the number of clusters in a dataset. An almost two‐valued function called cluster‐boundary indicator is introduced with the use of similarity‐based information‐theoretic sample entropy and probability descriptions. This function finds inter‐cluster boundary samples for cluster availability in a single iteration. Experiments with synthetic and anonymous real datasets show that the new algorithm outperforms its major counterparts statistically in terms of time complexity and the number of clusters found successfully.