Data Mining and Knowledge Engineering

S. C. Punitha ¹, R. Jayasree ²

Affiliations
1 Department of Computer Science, P.S.G.R. Krishnammal College for Women, Coimbatore, IN
2 P.S.G.R Krishnammal College for Women, Coimbatore, IN

Abstract

In this paper extracting the relations between verbs and their arguments in the same sentence has the potential for analyzing terms within a sentence. A novel concept-based mining model is proposed. This paper model captures the semantic structure of each term within a sentence and document rather than the frequency of the term within a document only. There are four models are present in this concept based mining model, they are sentence-based concept analysis, Document-based concept analysis, Corpus-based and then concept based similarity measures are used. Here clustering is used to attained the better results of the text mining. Spherical Gaussian EM algorithm clustering techniques are used. Large sets of experiments using the proposed concept-based mining model on different data sets in text clustering are conducted. Effectiveness of concept matching in determining an accurate measure of the similarity between documents and extensive sets of experiments using the concept-based term analysis and similarity measure are conducted. Experimental results are taken using MATLAB. Three types of datasets are used in this paper they are Reuters, TDT and 20 News Group. Performance evaluation are used for the text clustering are F-Measure and Execution Time.

Keywords

Data Preprocessing, Web Usage Mining, Path Completion Algorithm, Data Cleaning, User Session Identification, Modified Expectation Maximation.

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S. C. Punitha ¹, G. Nathiya ², Punithavalli

Affiliations
1 Department of Computer Science and Information Technology, P. S. G. R Krishnammal College for Women, Coimbatore, IN
2 Department of Computer Science and Information Technology, P.S.G.R Krishnammal College for Women, Coimbatore, IN

Abstract

Clustering is an unsupervised learning method that constitutes a cornerstone of an intelligent data analysis process. It is used for the exploration of inter-relationships among a collection of patterns, by organizing them into homogeneous clusters. Clustering has been dynamically applied to a variety of tasks in the field of Information Retrieval (IR). Clustering has become one of the most active area of research and the development. Clustering attempts to discover the set of consequential groups where those within each group are more closely related to one another than the others assigned to different groups. The resultant clusters can provide a structure for organizing large bodies of text for efficient browsing and searching. There exists a wide variety of clustering algorithms that has been intensively studied in the clustering problem. Among the algorithms that remain the most common and effectual, the iterative optimization clustering algorithms have been demonstrated reasonable performance for clustering, e.g. the Expectation Maximization (EM) algorithm and its variants, and the well known k-means algorithm. This paper presents an analysis on how partition method clustering techniques-EM and K-means algorithm work on heartspect dataset with below mentioned features-Purity, Entropy, CPU time, Cluster wise analysis, Mean value analysis and inter cluster distance. Thus the paper finally provides the experimental results of datasets for five clusters to strengthen the results that the quality of the behavior in clusters in EM algorithm is far better than k-means algorithm.

Keywords

Cluster, EM, K-Means, Purity, Entropy, Purity, Entropy, Cluster Wise Analysis and Mean Value Analysis.

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