VIRTUAL MINING MODEL FOR CLASSIFYING TEXT USING UNSUPERVISED LEARNING

In real world data mining is emerging in various er a, one of its most outstanding performance is held in various research such as Big data, multimedia minin g, text mining etc. Each of the researcher proves t heir contribution with tremendous improvements in their proposal by means of mathematical representation. Empowering each problem with solutions are classified into mathematical and implementation models. The mathematical model relates to the straight forward rules and formulas that are related to the problem definition of particular field of domain. Whereas t he implementation model derives some sort of knowledge from the real time decision making behaviour such a s artificial intelligence and swarm intelligence an d has a complex set of rules compared with the mathematical model. The implementation model mines and derives knowledge model from the collection of data set and attributes. This knowledge is applied to th e concerned problem definition. The objective of our work is to efficiently mine knowledge from the unstructured text documents. In order to mine textu al documents, text mining is applied. The text mini ng is the sub-domain in data mining. In text mining, the proposed Virtual Mining Model (VMM) is defined for effective text clustering. This VMM involves the le arning of conceptual terms; these terms are grouped in Significant Term List (STL). VMM model is appropriate combination of layer 1 arch with Analysis of Bilateral Intelligence (ABI). The frequent update o f conceptual terms in the STL is more important for effective clustering. The result is shown, Artifial neural network based unsupervised learning algorit hm is used for learning texual pattern in the Virtual Min ing Model. For learning of such terminologies, this paper proposed Artificial Neural Network based learning a lgorithm.

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