Fuzzy Clustering Methods in Data Mining: A Comparative Case Analysis
- Resource Type
- Conference
- Authors
- Raju, G.; Thomas, Binu; Tobgay, Sonam; Kumar, Shanta
- Source
- 2008 International Conference on Advanced Computer Theory and Engineering Advanced Computer Theory and Engineering, 2008. ICACTE '08. International Conference on. :489-493 Dec, 2008
- Subject
- Communication, Networking and Broadcast Technologies
Computing and Processing
Signal Processing and Analysis
Components, Circuits, Devices and Systems
Clustering methods
Data mining
Clustering algorithms
Partitioning algorithms
Fuzzy logic
Uncertainty
Algorithm design and analysis
Iterative algorithms
Fuzzy sets
Machine learning algorithms
fuzzy c-means
k-means
fuzzy logic
clustering
- Language
- ISSN
- 2154-7491
2154-7505
The conventional clustering algorithms in data mining like k-means algorithm have difficulties in handling the challenges posed by the collection of natural data which is often vague and uncertain. The modeling of imprecise and qualitative knowledge, as well as handling of uncertainty at various stages is possible through the use of fuzzy sets. Fuzzy logic is capable of supporting to a reasonable extent, human type reasoning in natural form by allowing partial membership for data items in fuzzy subsets. Integration of fuzzy logic in data mining has become a powerful tool in handling natural data. In this paper we introduce the concept of fuzzy clustering and also the benefits of incorporating fuzzy logic in data mining. Finally this paper provides a comparative analysis of two fuzzy clustering algorithms namely fuzzy c-means algorithm and adaptive fuzzy clustering algorithm.