Accuracy of clustering prediction of PAM and K-modes algorithms

Marc Gregory Dixon, Stanimir Genov, Vasil Hnatyshin, Umashanger Thayasivam

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

2 Scopus citations


The concept of grouping (or clustering) data points with similar characteristics is of importance when working with the data that frequently appears in everyday life. Data scientists cluster the data that is numerical in nature based on the notion of distance, usually computed using Euclidean measure. However, there are many datasets that often consists of categorical values which require alternative methods for grouping the data. That is why clustering of categorical data employs methods that rely on similarity between the values rather than distance. This work focuses on studying the ability of different clustering algorithms and several definitions of similarity to organize categorical data into groups.

Original languageEnglish (US)
Title of host publicationAdvances in Information and Communication Networks - Proceedings of the 2018 Future of Information and Communication Conference FICC, Vol. 1
EditorsRahul Bhatia, Kohei Arai, Supriya Kapoor
PublisherSpringer Verlag
Number of pages16
ISBN (Print)9783030034016
StatePublished - Jan 1 2019
EventFuture of Information and Communication Conference, FICC 2018 - Singapore, Singapore
Duration: Apr 5 2018Apr 6 2018

Publication series

NameAdvances in Intelligent Systems and Computing
ISSN (Print)2194-5357


OtherFuture of Information and Communication Conference, FICC 2018

All Science Journal Classification (ASJC) codes

  • Control and Systems Engineering
  • General Computer Science


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