FUZZY CLUSTERING ANALYSIS OF DATA MINING: APPLICATION TO AN ACCIDENT MINING SYSTEM

This paper is concerned with the application of data transforms and fuzzy clustering to extract useful data. It is possible to distinguish similar information which includes selector and removes clusters of less importance with respect to describing the data. Clustering takes place in the product space of systems inputs and outputs and each cluster corresponds to a fuzzy IF-THEN rule. By initializing the clustering with a number of clusters and subsequently removing less important ones as the clustering progresses, it is sought to obtain a suitable partition of the data in an automated manner. The approach is generally applicable to the fuzzy-means and related algorithms. In this paper, this method can better return appropriate information for user queries; in particular, a novel ranking strategy is provided to measure the relevance score of an annotated set of web results by considering user queries, data annotation, and the underlying ontology.