Automatic Clustering Subspace for High Dimensional Categorical Data Using Neuro-Fuzzy Classification

Clustering has been used extensively as a vital tool of data mining. Data gathering has been deliberated widely, but mostly all identified usual clustering algorithms lean towards to break down in high dimensional spaces because of the essential sparsely of the data points. Present subspace clustering methods for handling high-dimensional data focus on numerical dimensions. The minimum spanning tree based clustering algorithms, because they do not adopt that data points are clustered around centers or split by a regular geometric curve and have been widely used in training. The present techniques allow these algorithms to extend much more easily with both the number of instances in the dataset and the number of attributes. But the performance minimize soon with the size of the subspaces in which the groups are found. The important parameter needed by these algorithms is the density threshold and it is not easy to set, particularly across all dimensions of the dataset. The aim of this paper is proposed method investigate the performance of different Neuro-Fuzzy classification methods for the distinction of benign and malign tissue in genes.