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Evaluation of Modified Categorical Data Fuzzy Clustering Algorithm on the Wisconsin Breast Cancer Dataset
Author
Source
Issue
Vol. 2016, Issue 2016 (31 Dec. 2016), pp.1-6, 6 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2016-02-24
Country of Publication
Egypt
No. of Pages
6
Main Subjects
Abstract EN
The early diagnosis of breast cancer is an important step in a fight against the disease.
Machine learning techniques have shown promise in improving our understanding of the disease.
As medical datasets consist of data points which cannot be precisely assigned to a class, fuzzy methods have been useful for studying of these datasets.
Sometimes breast cancer datasets are described by categorical features.
Many fuzzy clustering algorithms have been developed for categorical datasets.
However, in most of these methods Hamming distance is used to define the distance between the two categorical feature values.
In this paper, we use a probabilistic distance measure for the distance computation among a pair of categorical feature values.
Experiments demonstrate that the distance measure performs better than Hamming distance for Wisconsin breast cancer data.
American Psychological Association (APA)
Ahmad, Amir. 2016. Evaluation of Modified Categorical Data Fuzzy Clustering Algorithm on the Wisconsin Breast Cancer Dataset. Scientifica،Vol. 2016, no. 2016, pp.1-6.
https://search.emarefa.net/detail/BIM-1117691
Modern Language Association (MLA)
Ahmad, Amir. Evaluation of Modified Categorical Data Fuzzy Clustering Algorithm on the Wisconsin Breast Cancer Dataset. Scientifica No. 2016 (2016), pp.1-6.
https://search.emarefa.net/detail/BIM-1117691
American Medical Association (AMA)
Ahmad, Amir. Evaluation of Modified Categorical Data Fuzzy Clustering Algorithm on the Wisconsin Breast Cancer Dataset. Scientifica. 2016. Vol. 2016, no. 2016, pp.1-6.
https://search.emarefa.net/detail/BIM-1117691
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1117691