An Efficient Concept-Based Mining Model for Enhancing Text Clustering

Abstract

Most of the common techniques in text mining are based on the statistical analysis of a term, either word or phrase. Statistical analysis of a term frequency captures the importance of the term within a document only. However, two terms can have the same frequency in their documents, but one term contributes more to the meaning of its sentences than the other term. Thus, the underlying text mining model should indicate terms that capture the semantics of text. In this case, the mining model can capture terms that present the concepts of the sentence, which leads to discovery of the topic of the document. A new concept-based mining model that analyzes terms on the sentence, document, and corpus levels is introduced. The concept-based mining model can effectively discriminate between nonimportant terms with respect to sentence semantics and terms which hold the concepts that represent the sentence meaning. The proposed mining model consists of sentence-based concept analysis, document-based concept analysis, corpus-based concept-analysis, and concept-based similarity measure. The term which contributes to the sentence semantics is analyzed on the sentence, document, and corpus levels rather than the traditional analysis of the document only. The proposed model can efficiently find significant matching concepts between documents, according to the semantics of their sentences. The similarity between documents is calculated based on a new concept-based similarity measure. The proposed similarity measure takes full advantage of using the concept analysis measures on the sentence, document, and corpus levels in calculating the similarity between documents. Large sets of experiments using the proposed concept-based mining model on different data sets in text clustering are conducted. The experiments demonstrate extensive comparison between the concept-based analysis and the traditional analysis. Experimental results demonstrate the substantial enhancement of the clustering quality using the sentence-based, document-based, corpus-based, and combined approach concept analysis.