An analysis on effective and accurate data clustering based on Non-negative Matrix Factorization

Nonnegative matrix factorization method is a kind of new matrix rotting method. It is an effective tool for large data processing and analysis. At the same time, NMF has an important performance on in intellectual information processing and pattern recognition. We then aim for increasing an efficiency and accuracy of data clustering and classification based on NMF. NMF method is used to reduce the length of the original matrix. There are k-means algorithms based on similarity, k – layered clustering algorithm, spherical k means algorithm in the existing clustering methods. In this paper using the fuzzy c-means (FCM) algorithm to find the nearest clusters for declaring the identity and accuracy of query sample. To choose cluster k properly to make distance between cluster and cluster of basis matrix. Keywords— NMF; Data clustering; Support Vector Machine; k-means; Fuzzy c-means;