A Comparative Study of Data Clustering Techniques
نویسنده
چکیده
Data clustering is a process of putting similar data into groups. A clustering algorithm partitions a data set into several groups such that the similarity within a group is larger than among groups. This paper reviews four of the most representative off-line clustering techniques: K-means clustering, Fuzzy Cmeans clustering, Mountain clustering, and Subtractive clustering. The techniques are implemented and tested against a medical problem of heart disease diagnosis. Performance and accuracy of the four techniques are presented and compared.
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