AN ONTOLOGY BASED DEEP ? MINING METHOD TO CLUSTER THE CONTENT BASED ON WEBLOG

ABSTRACT In our work, the text data of text mining has gradually become a new follow a line of investigation. Text clustering can greatly simplify browsing large collections of documents by reorganizing them into a smaller number of patterns in text documents manageable clusters. Text clustering is mainly used for a document clustering system which clusters the set of documents based on the user typed key term.Firstly the system pre-processes the set of documents and the user given terms. We use the feature evaluation to reduce the dimensionality of high-dimensional text vector. The system then identifies the term frequency and then those frequencies are weighted by using the inverted document frequency method. Then this weight of documents is used for clustering. Feature clustering is a powerful method to reduce the dimensionality of feature vectors for text classification. Presents an innovative and effective pattern discovery technique which includes the processes of pattern deploying and pattern evolving. In this paper, we propose a fuzzy similarity-based self-constructing algorithm for feature clustering.