New Approach for Data classification using Multi view graph learning Technique

Text classification approach gaining more importance because of the accessibility of large number of electronic documents from a variety of resource. Text categorization (Also called Text Categorization) is the task of assigning predefined categories to documents. It is the method of finding interesting regularities in large textual, where interesting means non trivial, hidden, previously unknown and potentially useful. The goal of text mining is to enable users to extract information from textual resource and deals with operation such as retrieval, classification, clustering, data mining, natural language preprocessing and machine learning techniques together to classify different pattern. A major characteristic or difficulty of text categorization is high dimensionality of feature space. The reduction of dimensionality by selecting new attributes which is subset of old attributes is known as feature selection. Feature selection methods are discussed in this paper for reducing the dimensionality of the dataset by removing features that are considered irrelevant for the classification. This paper surveys of text classification, several approaches of text classification, feature selection methods and applications of text classification. An increasing number of data mining tasks includes the analysis of complex and structured types of data and make use of expressive pattern languages. Most from these applications can't be solved using traditional data mining algorithms. This is the cause of main motivation for the multi-disciplinary field of Multi-Relational Data Mining (MRDM).