An Affinity Propagation Clustering Algorithm for Mixed Numeric and Categorical Datasets

Clustering has been widely used in different fields of science, technology, social science, and so forth. In real world, numeric as well as categorical features are usually used to describe the data objects. Accordingly, many clustering methods can process datasets that are either numeric or categor...

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Vydáno v:Mathematical problems in engineering Ročník 2014; číslo 2014; s. 1 - 8
Hlavní autoři: Zhang, Kang, Gu, Xingsheng
Médium: Journal Article
Jazyk:angličtina
Vydáno: Cairo, Egypt Hindawi Publishing Corporation 01.01.2014
John Wiley & Sons, Inc
Témata:
Adaptive algorithms
Affinity
Algorithms
Cluster analysis
Clustering
Clusters
Data mining
Data processing
Datasets
Mathematical models
Methods
Performance evaluation
Propagation
Similarity
Studies
ISSN:1024-123X, 1563-5147
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Shrnutí:Clustering has been widely used in different fields of science, technology, social science, and so forth. In real world, numeric as well as categorical features are usually used to describe the data objects. Accordingly, many clustering methods can process datasets that are either numeric or categorical. Recently, algorithms that can handle the mixed data clustering problems have been developed. Affinity propagation (AP) algorithm is an exemplar-based clustering method which has demonstrated good performance on a wide variety of datasets. However, it has limitations on processing mixed datasets. In this paper, we propose a novel similarity measure for mixed type datasets and an adaptive AP clustering algorithm is proposed to cluster the mixed datasets. Several real world datasets are studied to evaluate the performance of the proposed algorithm. Comparisons with other clustering algorithms demonstrate that the proposed method works well not only on mixed datasets but also on pure numeric and categorical datasets.
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ISSN:1024-123X
1563-5147
DOI:10.1155/2014/486075