A Comparative Study of Clustering Algorithms for Mixed Datasets

Saad Harous, Maryam Al Harmoodi, Hessa Biri

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

1 Citation (Scopus)

Abstract

Clustering groups, a set of elements in a manner that elements in the same categoryhave more common characteristics (based on a given set of attributes) among themthan to elements in other categories. Each group is called a cluster. Clustering is used in many areas: sensor networks, social networks, health, business and other applications. There are many different clustering algorithms with different parameters. The appropriate clustering algorithm and parameter settings depend on data set and the problem being solved. Some work only on numerical data and other on mixed data. Our aim is to do a comparative study of these algorithms.

Original languageEnglish
Title of host publicationProceedings - 2019 Amity International Conference on Artificial Intelligence, AICAI 2019
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages484-488
Number of pages5
ISBN (Electronic)9781538693469
DOIs
Publication statusPublished - Apr 26 2019
Event2019 Amity International Conference on Artificial Intelligence, AICAI 2019 - Dubai, United Arab Emirates
Duration: Feb 4 2019Feb 6 2019

Publication series

NameProceedings - 2019 Amity International Conference on Artificial Intelligence, AICAI 2019

Conference

Conference2019 Amity International Conference on Artificial Intelligence, AICAI 2019
Country/TerritoryUnited Arab Emirates
CityDubai
Period2/4/192/6/19

Keywords

  • Clustering
  • K Means
  • Mixed Data
  • Similarity measure.

ASJC Scopus subject areas

  • Artificial Intelligence

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