Survey of State-of-the-Art Mixed Data Clustering Algorithms

Amir Ahmad, Shehroz S. Khan

Research output: Contribution to journalArticlepeer-review

79 Citations (Scopus)

Abstract

Mixed data comprises both numeric and categorical features, and mixed datasets occur frequently in many domains, such as health, finance, and marketing. Clustering is often applied to mixed datasets to find structures and to group similar objects for further analysis. However, clustering mixed data are challenging because it is difficult to directly apply mathematical operations, such as summation or averaging, to the feature values of these datasets. In this paper, we present a taxonomy for the study of mixed data clustering algorithms by identifying five major research themes. We then present the state-of-the-art review of the research works within each research theme. We analyze the strengths and weaknesses of these methods with pointers for future research directions. At last, we present an in-depth analysis of the overall challenges in this field, highlight open research questions, and discuss guidelines to make progress in the field.

Original languageEnglish
Article number8662561
Pages (from-to)31883-31902
Number of pages20
JournalIEEE Access
Volume7
DOIs
Publication statusPublished - 2019

Keywords

  • Categorical features
  • clustering
  • mixed datasets
  • numeric features

ASJC Scopus subject areas

  • Computer Science(all)
  • Materials Science(all)
  • Engineering(all)

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