Survey of State-of-the-Art Mixed Data Clustering Algorithms
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...
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| Vydáno v: | IEEE access Ročník 7; s. 31883 - 31902 |
|---|---|
| Hlavní autoři: | , |
| Médium: | Journal Article |
| Jazyk: | angličtina |
| Vydáno: |
Piscataway
IEEE
2019
The Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Témata: | |
| ISSN: | 2169-3536, 2169-3536 |
| On-line přístup: | Získat plný text |
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| 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. |
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| AbstractList | 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. |
| Author | Khan, Shehroz S. Ahmad, Amir |
| Author_xml | – sequence: 1 givenname: Amir orcidid: 0000-0002-9062-8170 surname: Ahmad fullname: Ahmad, Amir email: amirahmad@uaeu.ac.ae organization: College of Information Technology, United Arab Emirates University, Al Ain, UAE – sequence: 2 givenname: Shehroz S. surname: Khan fullname: Khan, Shehroz S. organization: Toronto Rehabilitation Institute, University Health Network, Toronto, ON, Canada |
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| Title | Survey of State-of-the-Art Mixed Data Clustering Algorithms |
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