An Affinity Propagation Clustering Algorithm for Mixed Numeric and Categorical Datasets
Joint Authors
Source
Mathematical Problems in Engineering
Issue
Vol. 2014, Issue 2014 (31 Dec. 2014), pp.1-8, 8 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2014-09-29
Country of Publication
Egypt
No. of Pages
8
Main Subjects
Abstract EN
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.
American Psychological Association (APA)
Zhang, Kang& Gu, Xingsheng. 2014. An Affinity Propagation Clustering Algorithm for Mixed Numeric and Categorical Datasets. Mathematical Problems in Engineering،Vol. 2014, no. 2014, pp.1-8.
https://search.emarefa.net/detail/BIM-1044298
Modern Language Association (MLA)
Zhang, Kang& Gu, Xingsheng. An Affinity Propagation Clustering Algorithm for Mixed Numeric and Categorical Datasets. Mathematical Problems in Engineering No. 2014 (2014), pp.1-8.
https://search.emarefa.net/detail/BIM-1044298
American Medical Association (AMA)
Zhang, Kang& Gu, Xingsheng. An Affinity Propagation Clustering Algorithm for Mixed Numeric and Categorical Datasets. Mathematical Problems in Engineering. 2014. Vol. 2014, no. 2014, pp.1-8.
https://search.emarefa.net/detail/BIM-1044298
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1044298