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Clustering mixed-type data using a probabilistic distance algorithm
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DOI：10.1016/j.asoc.2022.109704.png)
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Cluster analysis is a broadly used unsupervised data analysis technique for finding groups of homoge-neous units in a data set. Probabilistic distance clustering adjusted for cluster size (PDQ), discussed in this contribution, falls within the broad category of clustering methods initially developed to deal with continuous data; it has the advantage of fuzzy membership and robustness. However, a common issue in clustering deals with treating mixed-type data: continuous and categorical, which are among the most common types of data. This paper extends PDQ for mixed-type data using different dissimilarities for different kinds of variables. At first, the PDQ for mixed-type data is defined, then a simulation design shows its advantages compared to some state of the art techniques, and ultimately, it is used on a real data set. The conclusion includes some future developments.(c) 2022 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).
Keyword:
Probabilistic distance clustering
Mixed-type data
Fuzzy clustering
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IF:
6.6
论文数:
1.4W
被引数:
4.8W
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