An Approach towards Data Clustering By Using NLP and Annotated Text Categorization

Aim is to develop system for clustering of data into user defines clusters with the help of language processing. The main objective behind this research is to solve the problem of data classification into large dataset to get an efficient system which classifies data not only on basis of the dataset, but also on basis of the property of keyword and specified class. This provides the best optimization and segmentation, which incorporate a priori knowledge of existing dataset. This will help end user to choose the item from the particular data cluster from its previous parches or search from the dataset. This field leads to: event resolution, grammar annotation, information mining, knowledgebase, labeling, question/answer, redundancy reduction, similarity measure, summarization, word sense disambiguation, and word sense induction. Implementation of application of Apriory algorithm on the given data to classify the data into the categories. Bisecting K-Means algorithm and hierarchical clustering used categorizing all objects in single cluster. PDDP is the latest development of SVD-based partitioning techniques. DOI: 10.17762/ijritcc2321-8169.15079