2023
DOI: 10.11591/ijece.v13i1.pp1048-1059
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Clustering heterogeneous categorical data using enhanced mini batch K-means with entropy distance measure

Abstract: <span lang="EN-US">Clustering methods in data mining aim to group a set of patterns based on their similarity. In a data survey, heterogeneous information is established with various types of data scales like nominal, ordinal, binary, and Likert scales. A lack of treatment of heterogeneous data and information leads to loss of information and scanty decision-making. Although many similarity measures have been established, solutions for heterogeneous data in clustering are still lacking. The recent entrop… Show more

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