Data Mining Lab Report

META-LEVEL INFORMATION EXTRACTION FROM TEXT DOCUMENTS USING GA-BASED COATES AND COLT ALGORITHM

Kanmani. K

Department of Computer Science and Engineering

M.I.E.T Engineering College

Trichy, India

Rama. B

Department of Computer Science and Engineering

M.I.E.T Engineering College

Trichy, India

Abstract— Information Retrieval (IR) system identifies the pages in the collection of documents which matches the user query. IR system allows us to narrow down the set of documents that are relevant to the particular text mining problem. Then perform text documents clustering based on Genetic Algorithm (GA) with mutation rate. The mutation operation is important to the achievement of GA since it enlarges the text documents search guidelines and

In this context the selection of characteristics and also the influence of domain knowledge and domain-specific procedures play an important role. Therefore, an adaptation of the known data mining algorithms to text data is usually necessary. In order to achieve this, one frequently relies on the experience and results of research in information retrieval, natural language processing and information extraction. In all of these areas we also apply data mining methods and statistics to handle their specific tasks:

Information Retrieval (IR): Information retrieval is the finding of documents which contain answers to questions and not the finding of answers itself. In order to achieve this goal statistical measures and methods are used for the automatic processing of text data and comparison to the given question. Information retrieval in the broader sense deals with the entire range of information processing, from data retrieval to knowledge

Schutze and C. Silverstein [27] has proposed projecting documents via LSI and truncation offers a dramatic advantage over full-profile clustering in terms of time efficiency. The improved efficiency, surprisingly, is not accompanied by a loss of cluster quality. In fact, with the exception of radical truncation (TF-20), there is no significant difference in the cluster quality of any of the projections was studied. This means that, in contrast to similarity search, clustering can proceed successfully even if vector representations have been reduced at a considerable loss of information. The result is show that clustering is less fine-grained task than similarity search and therefore requires less precision in determining the distances of objects with respect to each