Research On Fuzzy Clustering Analysis In Data Mining

Data Mining is different from traditional data processing techniques, because it can analyse and pick up useful knowledge from a mass of information, which can help man make correct decision. Data Mining is a superior area in the information and database technology, and is usually considered as one of the key technology with wild developing perspective.Being the most important techniques of Data Mining, clustering analysis is more and more attentioned. Clustering analysis is a frequently-used technique which can classifying data in reason, at present, there are many clustering analysis techniques, this paper researches on Fuzzy Clustering-Means algorithm, and proposes an improved algorithm based on the disadvantage of FCM.Firstly, since the condition that the sum of possible membership degree of data set is 1 will make negative effect on the correction ratio of fuzzy clustering in fuzzy events, some research on the membership degree of data based on uncertain the theory. Possible membership degree and uncertain membership degree are introduced into this algorithm's object function, which makes the element sample not longer belong to one cluster only and leads to more preferable results than current clustering algorithms. Secondly, using Mahalanobis space widens the application of the FCM. Lastly, changing the object vector to matrix adepts the algorithm to more data model.Through the experiments, the improved algorithm achieves expected purpose.