VIRTUAL MINING MODEL FOR CLASSIFYING TEXT USING UNSUPERVISED LEARNING
S. Koteeswaran, E. Kannan and P. Visu
DOI : 10.3844/ajassp.2014.764.768
American Journal of Applied Sciences
Volume 11, Issue 5
In real world data mining is emerging in various era, one of its most outstanding performance is held in various research such as Big data, multimedia mining, text mining etc. Each of the researcher proves their 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 the implementation model derives some sort of knowledge from the real time decision making behaviour such as artificial intelligence and swarm intelligence and has a complex set of rules compared with the mathematical model. The implementation model mines and derives knowledge model from the collection of dataset and attributes. This knowledge is applied to the concerned problem definition. The objective of our work is to efficiently mine knowledge from the unstructured text documents. In order to mine textual documents, text mining is applied. The text mining 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 learning 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 of conceptual terms in the STL is more important for effective clustering. The result is shown, Artifial neural network based unsupervised learning algorithm is used for learning texual pattern in the Virtual Mining Model. For learning of such terminologies, this paper proposed Artificial Neural Network based learning algorithm.
© 2014 S. Koteeswaran, E. Kannan and P. Visu. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.