Cloud computing is a technology that provides efficient services to the users over internet. Users stores volumes of data in cloud which is rendered as data as a service (DaaS) on demand and charged as per usage. Text mining is a technology that is used to retrieve data from a massive set of database. Cloud uses Text mining to retrieve data efficiently from various cloud data centres. Text classification is a technique used for discovering classes of indefinite data. Prior to applying any mining technique, trivial features should be filtered. Feature selection is capable of improving learning process, lesser computational complexity, organizes better general models, and decreasing required storage. We analyses towards effectiveness of the clustering based feature selection method. This paper is to analysis on different techniques used for feature selection. Further survey on Feature selection and Feature extraction technique has been extract the features from the documents, which results in single and multi-label document classification. Based on the extracted features the survey is done on multiple-feature based projective nonnegative matrix factorization technique to cluster the documents.

Data mining, Feature selection, Text mining, Filtering, factorization

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