Browsing by Author "Rajakumar, K.S."

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    Maximum frequent item set based clustering algorithm for big text data
    (Blue Eyes Intelligence Engineering and Sciences Publication, 2019) Kanimozhi, K.V.; Rajakumar, K.S.; Venkatesan, M.
    Due to fast growth of internet and continuous expansion of World Wide Web like digital libraries, online news contributes to massive amount of electronic unstructured text documents on the web. Although lot traditional techniques are available to extract the knowledge from large collection of text documents, still to improve precision of the web search retrieval and to find most appropriate documents from huge text collections proficiently is a big challenge. Clustering techniques helps the search engine to retrieve the documents. The proposed system overcomes existing problems using bivariate n-gram frequent item clustering algorithm by concept of maximum frequent set which maintain the sequence and meaning of sentence in order to reduce huge dimension and and frequent item sets finds similarity. Then based on maximum document occurrence we cluster the documents. Thus our method obtains quality of clusters when compared with existing methodologies and improves the efficiency. The experiment is shown for sample Newsgroup dataset for existing K-Mean and FICMDO (Frequent item clustering method based on maximum document occurrence) and proved the f-measure is higher for our algorithm. Since the f-measure increases, obtains efficient clusters. Hence it is faster and efficient big data method which improves the performance when compared with vector space model like K-Means algorithm. © BEIESP.
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    Weighted frequent pattern based agglomerative clustering for large unstructured text data
    (Science and Engineering Research Support Society ijbsbt@sersc.org PO Box 5014Sandy Bay TAS 7005 Tasmania, 2020) Kanimozhi, K.V.; Rajakumar, K.S.; Venkatesan, M.
    Processing large amount of text using traditional clustering methods are key challenges.Research communities have proposed the various clustering approaches for analyzing unstructured data. Frequent item based clustering method is one of the mostly used clustering for text analytic domain. An approach based on Frequent Weighted Utility Itemsets (FWUI) and then clustering using the MC (Maximum Capturing) algorithm is one of the most effective methods for text clustering. However, the Maximum Capturing clusteringAlgorithm based on the similarity matrix leads to a lot of irrelevant clusters that aren’t desired. In this work, Weighted Frequent Pattern based Agglomerative Clustering(WFUP_AC)is proposed for clustering large text data.First, the Term Frequency (TF) is calculated for each term in the documents to create a weight matrix for all documents. The weights of terms in documents are based on the Inverse Document Frequency. The WFUP algorithm is applied for mining Weighted Frequent Utility Pattern (WFUP) from a number matrix and the weights of terms in documents. Then based on frequent utility itemsets, a similarity matrix is obtained for each document where each entry equals to common frequent itemset between two documents. Then distance matrix is calculated from the similarity matrix, finally Hierarchical Agglomerative Clustering method is applied on the Distance matrix using complete linkage and cut the dendrogram as per the need. Our proposed method has been evaluated on two text document data sets like newsgroup and Reuters data sets with different size consisting of 100,300,500 and 1000 documents. The experimental results show that our method, weighted frequent pattern based agglomerative clustering (WFUP_AC) improves the accuracy of the text clustering compared to MC clustering methods using FIs(Frequent Itemset) and FWUIs. © 2020 SERSC.