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 datacollection methodologies have led to the proliferation ofuncertain data. Such data points are often represented in theform of a probabilistic function, since the correspondingdeterministic value is not known. The modeling of impreciseand qualitative knowledge, as well as handling of uncertaintyat 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 partialmembership for data items in fuzzy subsets. Integration offuzzy logic and kl divergence in data mining has become apowerful tool in handling natural data. Introduce the conceptof fuzzy clustering and also the benefits of incorporatingfuzzy logic with kl divergence in data mining. Finally itprovides a comparative analysis of fuzzy clusteringalgorithms namely association rule based fuzzy.