The Research And Applications Of Fuzzy Clustering Analysis In Data Mining

With the rapid development of the network and database technology, the ability of data acquiring has been increasingly incompatible with the ability of data analysis,so an automatic technology which can process data in a deep level is needed. Data mining is such a technology. The data mining is to distill the interesting knowledge from the data in the large database, and all these knowledge is connotative, undiscovered and potential useful information, and the achieved knowledge is expressed as concepts, rule, regularities, patterns and so on.. Clustering is one of the most important branches of data mining research work. It is an unsupervised classification, and it is an important method with which people know the society and nature. In the real world, the boundaries between objects are usually fuzzy when we classify them. Therefore, the fuzzy clustering technology has been brought forward. In this paper, the basic concepts and related knowledge of data mining, clustering and fuzzy theory is presented. The principles and procedures of the clustering based on fuzzy equivalence relation is analyzed in detail. Then the clustering algorithm and FCBER program developed to realize it is discussed. Furthermore, a new algorithm named Grid-Based Algorithm is proposed, which is a totally different method compared with the traditional Distance-Based Approach. According to this algorithm, a multi-dimensional data space is partitioned into many grids, and the dense regions are formed by computing the density of grids. Then these dense regions will be converged into clusters by calculating the attraction of near grids. The advantage of this algorithm is that it can automatically find out subspaces containing interesting patterns and discover all clusters in that subspace. Besides, it performs well when dealing with high dimensional data and has good scalability when the size of the data sets increases. In recent...