A study on two-stage mixed attribute data clustering based on density peak
Joint Authors
Liu, Shihua
Liu, Xianghua
Zhang, Hao
Source
The International Arab Journal of Information Technology
Issue
Vol. 18, Issue 5 (30 Sep. 2021), pp.634-643, 10 p.
Publisher
Zarqa University Deanship of Scientific Research
Publication Date
2021-09-30
Country of Publication
Jordan
No. of Pages
10
Main Subjects
Information Technology and Computer Science
Abstract EN
A Two-stage clustering framework and a clustering algorithm for mixed attribute data based on density peaks and Goodall distance are proposed.
Firstly, the subset of numerical attributes of the dataset is clustered, and then the result is mapped into one-dimensional categorical attribute and added to the subset of categorical attribute data.
Finally, the new dataset is clustered by the density peaks clustering algorithm to obtain the final result.
Experiments on three commonly used UCI datasets show that this algorithm can effectively realize mixed attribute clustering and produce better clustering results than the traditional K-prototypes algorithm do.
The clustering accuracy on the Acute, Heart and Credit datasets are 17%, 24%, and 21% higher on average than that of the K-prototypes, respectively .
American Psychological Association (APA)
Liu, Shihua& Zhang, Hao& Liu, Xianghua. 2021. A study on two-stage mixed attribute data clustering based on density peak. The International Arab Journal of Information Technology،Vol. 18, no. 5, pp.634-643.
https://search.emarefa.net/detail/BIM-1431103
Modern Language Association (MLA)
Liu, Shihua…[et al.]. A study on two-stage mixed attribute data clustering based on density peak. The International Arab Journal of Information Technology Vol. 18, no. 5 (Sep. 2021), pp.634-643.
https://search.emarefa.net/detail/BIM-1431103
American Medical Association (AMA)
Liu, Shihua& Zhang, Hao& Liu, Xianghua. A study on two-stage mixed attribute data clustering based on density peak. The International Arab Journal of Information Technology. 2021. Vol. 18, no. 5, pp.634-643.
https://search.emarefa.net/detail/BIM-1431103
Data Type
Journal Articles
Language
English
Notes
Text in English ; abstracts in .
Record ID
BIM-1431103