FUZZY REGRESSION BASED CLUSTERING ON UNCERTAIN DATA - Ramya, R.; Kalpana, Dr.R (IJRDO -Journal of Computer Science Engineering, 2015)
In recent centuries, a number of indirect data collection methodologies have led to the proliferation of uncertain data. Such data points are often represented in the form of a probabilistic function, since the corresponding deterministic value is not known. 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 and kl divergence in data mining has become a powerful tool in handling natural data. Introduce the concept of fuzzy clustering and also the benefits of incorporating fuzzy logic with kl divergence in data mining. Finally it provides a comparative analysis of fuzzy clustering algorithms namely association rule based fuzzy.