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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Veröffentlicht in:IEEE access Jg. 7; S. 31883 - 31902
Hauptverfasser: Ahmad, Amir, Khan, Shehroz S.
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Piscataway IEEE 2019
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:
Algorithms
Categorical features
Clustering
Clustering algorithms
Cost function
Datasets
Education
Guidelines
Hamming distance
mixed datasets
numeric features
Partitioning algorithms
State-of-the-art reviews
Taxonomy
ISSN:2169-3536, 2169-3536
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Beschreibung
Zusammenfassung: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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ISSN:2169-3536
2169-3536
DOI:10.1109/ACCESS.2019.2903568