Data Mining and Knowledge Engineering

Apeksha Charola ¹, Sahista Machchhar ²

Affiliations
1 Department of Computer Engineering, Marwadi Education Foundation Group of Institute, Rajkot, IN
2 Department of Computer Engineering, Marwadi Education Foundation Group of Institutions, Rajkot, IN

Abstract

This paper presents a novel technique by combining regular clustering techniques with information extracted from WordNet. There are two approaches for traditional clustering algorithms utilize in documents clustering area. First approach work with documents as bag of words and consider each term as independent (means ignore semantic relationships between words). Second approach can determine semantics using WordNet. The proposed technique isutilizing second approach with different (identity, synonym, direct hypernym and meronym relation) & weighted (identity>synonym>direct hypernym>meronym) semantic relation. Concepts are weighted by generating concepts chain of related concepts. It utilizes the WordNet in turn to create low dimensional vector space which allows to build an efficient clustering technique. The proposed technique can improve cluster quality as well as achieve low dimensional vector space compared to other techniques.

Keywords

Document Clustering, K-Means Algorithm, WordNet, Concept Weighting, Synonym, Hypernym, Meronym.

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Apeksha Charola ¹, Sahista Machchhar ²

Affiliations
1 Marwadi Education Foundation Group of Institute, Gujarat Technological University, Ahmedabad, Gujarat, IN
2 Department of Computer Engineering, Marwadi Education Foundation Group of Institutions, Rajkot, IN

Abstract

With the problem of increased utilization of internet and the huge amount of text documents,the necessity of having efficient document clustering technology. In the field of Text mining, most work going on information retrieval and document summarization, very lessattention in domain of document clustering. Traditional text mining represents document as bag of words which has some limitation, as this method does not consider semantic relationship among the texts. Semantic text mining can overcome this limitation-using Ontology. Using ontology document represents as vector of weighted concepts. In this paper, present two type of survey. First is, Survey on pre-clustering approach. Second is, Documents clustering techniques.

Keywords

Document Clustering, Ontology, Pre-Clustering, Semantic, Text Mining, Weighted Concepts.

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