FUZZY REGRESSION BASED CLUSTERING ON UNCERTAIN DATA
Abstract
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.
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