Biclustering similarity measures for heterogeneous data

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dc.contributor.authorPornpimol Chaiwuttisak
dc.contributor.authorClarisse Dhaenens
dc.contributor.authorLaetitia Jourdan
dc.contributor.authorMaxence Vandromme
dc.date.accessioned2025-07-21T05:59:12Z
dc.date.issued2018-01-01
dc.description.abstractA similarity measure or distance is a successful key in data mining process and knowledge discovery including biclustering. Many measures are proposed on the specific data type. However, there are different types and characteristics of data in real world. In the paper, we study the performance of a variety of measures on different kind of data and then combine them to evaluate the quality of biclusters.
dc.identifier.doi10.1063/1.5055436
dc.identifier.urihttps://dspace.kmitl.ac.th/handle/123456789/7013
dc.subjectBiclustering
dc.subjectSimilarity (geometry)
dc.subjectSimilarity measure
dc.subject.classificationRough Sets and Fuzzy Logic
dc.titleBiclustering similarity measures for heterogeneous data
dc.typeArticle

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