A New Approach to Classify Text based on CosFuzzy Logic

Objective type of Examination evaluation is easy in Computer world. But the descriptive type of question evaluation is more difficult and there is no significant research has been taken place. In this paper I propose a new solution to the above problem with text classification using the new fuzzy logic named CosFuzzy Logic. Document Clustering is a useful technique that organizes a large quantity of unordered text documents into a small number of meaningful and coherent clusters, thereby providing a basis for intuitive and informative navigation and browsing mechanisms. Partitional clustering algorithms have been recognized to be more suitable as opposed to the hierarchical clustering schemes for processing large datasets. A wide variety of distance functions and similarity measures have been used for clustering, such as squared Euclidean distance, cosine similarity, and relative entropy. A Novel Fuzzy based feature clustering was proposed in which Gaussian distribution is used for fuzzy membership function. Clustering the data for four known classes, we used cosine similarity function along with fuzzy logic to calculate the similarity between two documents. We found that Experimental results show that our Cosfuzzy logic obtain better results. General Terms Feature Clustering, cosine similarity, Split distribution, fuzzy clustering.