A Survey on Improving the Clustering Performance in Text Mining for Efficient Information Retrieval
|International Journal of Engineering Trends and Technology (IJETT)||
|© 2014 by IJETT Journal|
|Year of Publication : 2014|
|Authors : S.Saranya , R.Munieswari
S.Saranya , R.Munieswari."A Survey on Improving the Clustering Performance in Text Mining for Efficient Information Retrieval", International Journal of Engineering Trends and Technology(IJETT), V8(5),249-256 February 2014. ISSN:2231-5381. www.ijettjournal.org. published by seventh sense research group
In recent years, the development of information systems in every field such as business, academics and medicine has led to increase in the amount of stored data year by year. A vast majority of data are stored in documents that are virtually unstructured. Text mining technology is very helpful for people to process huge information by imposing structure upon text. Clustering is a popular technique for automatically organizing a large collection of text. However, in real application domains, the experimenter possesses some background knowledge that helps in clustering the data. Traditional clustering techniques are rather unsuitable of multiple data types and cannot handle sparsity and high dimensional data. Co-clustering techniques are adopted to overcome the traditional clustering technique by simultaneously performing document and word clustering handling both deficiencies. Semantic understanding has become essential ingredient for information extraction, which is made by adopting constraints as a semi-supervised learning strategy. This survey reviews on the constrained co-clustering strategies adopted by researchers to boost the clustering performance. Experimental results using 20-Newsgroups dataset shows that the proposed method is effective for clustering textual documents. Furthermore, the proposed algorithm consistently outperformed all the existing constrained clustering and coclustering methods under different conditions.
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Clustering Techniques, Co-Clustering, Constrained Clustering, Semisupervised Learning, Text Mining.