Fuzzy sets, uncertainty, and information
Fuzzy sets, uncertainty, and information
Data mining and knowledge discovery in databases
Communications of the ACM
The data warehouse and data mining
Communications of the ACM
Classification with Degree of Membership: A Fuzzy Approach
ICDM '01 Proceedings of the 2001 IEEE International Conference on Data Mining
Mining Fuzzy Rules in A Donor Database for Direct Marketing by a Charitable Organization
ICCI '02 Proceedings of the 1st IEEE International Conference on Cognitive Informatics
Data Mining: Concepts and Techniques
Data Mining: Concepts and Techniques
Data mining in soft computing framework: a survey
IEEE Transactions on Neural Networks
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Recent developments in the fields of business investment, scientific research and information technology have resulted in the collection of massive data which becomes highly useful in finding certain patterns governing the data source. Clustering algorithms are popular in finding hidden patterns and information from such repository of data. The conventional clustering algorithms have difficulties in handling the challenges posed by the collection of natural data which is often vague and uncertain. This paper presents the concept of fuzzy clustering (fuzzy c-means clustering) and shows how it can handle vagueness and uncertainty in comparison with the conventional k-means clustering algorithm.