Implementation of Fuzzy K-Means in Multi-Type Feature Coselection for Clustering

Provided by: International Journal of Soft Computing and Engineering (IJSCE)
Topic: Data Management
Format: PDF
Information or knowledge can be conceptualized as data. It reflects in the data norm, the size and dimensions have improved high and more. Feature Selection is a preprocessing technique in supervised learning for improving predictive accuracy while reducing dimension in clustering and categorization. Multitype Feature Coselection for Clustering (MFCC) with hard k means is the algorithm which uses intermediate results in one type of feature space enhancing feature selection in other spaces, better feature set is co selected by heterogeneous features to produce better cluster in each space.

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