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RELEVANCE FEATURE DISCOVERY FOR TEXT MINING

ABSTRACT

In this paper we introduce a method to select irrelevant documentsfor weighting features.We continued todevelop the RFD model and experimentally prove that theproposed specificity function is reasonable and the termclassification can be effectively approximated by a featureclustering method. This paper presents an innovative model for relevance feature discovery. It discovers both positive and negativepatterns in text documents as higher level features and deploys them over low-level features (terms). It also classifies terms intocategories and updates term weights based on their specificity and their distributions in patterns. Substantial experiments using thismodel on RCV1, TREC topics and Reuters-21578 show that the proposed model significantly outperforms both the state-of-the-artterm-based methods and the pattern based methods.

EXISTING SYSTEM	PROPOSED SYSTEM
EXISTING CONCEPT:- Most existing popular text mining and classification methods have adopted term-based approaches. However, they have all suffered from the problems of polysemy and synonymy. Over the years, there has been often held the hypothesis that pattern-based methods should perform better than term-based ones in describing user preferences.	PROPOSED CONCEPT:- An innovative model for relevance feature discovery. It discovers both positive and negative patterns in text documents as higher level features and deploys them over low-level features (terms). It also classifies terms into categories and updates term weights based on their specificity and their distributions in patterns.
EXISTING ALGORITHM:- WFeature	PROPOSED ALGORITHM:- FClustering
ALGORITHM DEFINITION:- In the initialization, the algorithm uses the most time (O(|T|)) finding the initial value of T-. The initialization can also be implemented in (O(|T|))  if a hash function is used for the containment test.	ALGORITHM DEFINITION:- Algorithm FClustering describes the process of feature clustering, where DP+ is the set of discovered patterns of D+ and DP- is the set of discovered patterns of D-.
DRAWBACKS:- Most existing popular text mining and classification methods haveadopted term-based approaches. All suffered from the problems of polysemy and synonymy.	ADVANTAGES:- It discovers both positive and negative patterns in text documents as higher level features and deploys them over low-level features (terms). It also classifies terms into categories and updates term weights based on their specificity and their distributions in patterns.