Feature Subset Selection Algorithm for Large Volumes of Data Based on Clustering

Provided by: International Journal of Advanced Research in Computer Science & Technology (IJARCST)
Topic: Data Management
Format: PDF
Clustering which tries to group a set of points into clusters such that points in the same cluster are more similar to each other than points in different type of clusters. In the generative clustering model, the form of parametric data generation is assumed, and the main goal in the maximum likelihood formulation is to find the parameters that maximize the probability (likelihood) of generation of the data given the model. The FAST algorithm works in two steps. The first step of the algorithm is, features are divided into clusters by using graph-theoretic clustering methods.

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