Data Clustering by using NLP and Annotated Text Categorization

Aim is to develop system for clustering of data into user describe clusters with the help of language processing. The main objective behind this examination is to resolve the difficultly of data organisation into huge dataset to get a well-organized system which categorises data not only on basis of the dataset, but also on basis of the property of keyword and definite class. This provides the best optimization and separation, which incorporate a priori knowledge of present dataset. This will help end user to select the item from the specific data cluster from its earlier parches or search from the dataset. This field leads to: event determination, grammar annotation, information mining, knowledgebase, classification, question/answer, redundancy reduction, similarity measure, summarization, word sense disambiguation, and word sense induction. Implementation of Apriori algorithm on the given data to classify the data into the categories. Bisecting K-Means algorithm and hierarchical clustering used classifying all objects in single cluster.