Text mining is an important research field which challenges to different strategies from Machine Learning, Natural Language Processing, and Information Retrieval (IR), Data Mining as well as Knowledge Management contribute strategies to the resolve major issues in data overload through Text mining, an important research field. Text mining is now a widespread move to keep associated with the rapid expansion of scientific literature. Text Mining is to extract unstructured (textual) data, obtain relevant statistical correlations from text, and hence make the system stored in the data visible to the different Statistical and Machine Learning Techniques. Data to extract summaries for both the terms stored in the records or to estimate excerpts based on words reflected in the texts. Hence, clusters of reports are used in texts to identify words. Text mining can typically "transform text into numbers" (substantial tuples), which can be adopted into certain examines such as statistical data. Text mining involves Pre-processing of catalogues of texts like Information Extraction, Term Retrieval, Document Classification and Storage of Transition Representation. A process of document mining involves a sequence of activities to execute to mine the data. The following activities are Document Pre-Processing, Feature Selection, Text Transformation. In this paper mainly focus on major FS Techniques for Text Classification and Clustering.
Volume 12 | Issue 2