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. The authors 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.