文章基本信息

标题：Information Retrieval System through Advance Data Mining Using Clustering Techniques
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作者：K.V. Sumathi ; R.Mohanapriya
期刊名称：International Journal of Innovative Research in Science, Engineering and Technology
印刷版ISSN：2347-6710
电子版ISSN：2319-8753
出版年度：2016
卷号：5
期号：11
页码：19209
DOI：10.15680/IJIRSET.2016.0511090
出版社：S&S Publications
摘要：Clustering is a useful data mining tool to handle information retrieval system can be clustered using anyof the clustering algorithm such as K-means, ROCK etc. The IR systems help to retrieve necessary information frommassive databases over the internet.In the emerging new wave of applications where people are the ultimate target oftext clustering methods, cluster labels are intended to be read and comprehended by humans. The primary objective ofa clustering method should be to focus on providing good, descriptive cluster labels in addition to optimizing traditionalclustering quality indicators such as document-to-group assignment. In yet other words: in document browsing, textclustering serves the main purpose of describing and summarizing a larger set of documents; the particular documentassignment is of lesser importance.Descriptive clustering is a problem of discovering diverse groups of semantically related documents describedwith meaningful, comprehensible and compact text labels. In this article, to implement a new type of clusteringmethods for information retrieval which focuses on revealing the structure of document collections, summarizing theircontent and presenting this content to a human user in a compact way. In our implementation of Description ComesFirst (DCF) are in two clustering algorithms. First one is applicable to search results clustering and in second one isDescriptive k-Means which is applicable to collections of several thousand short and medium documents. Experimentalresults show the performance and scalability are more efficient than existing works.