Browsing by Author "V, Preamsudha"

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    A COMPARATIVE STUDY OF FUZZY MODELS IN DOCUMENT CLUSTERING
    (International Journal on Computer Science and Engineering, 2012-11) G, Manimekalai; K, Sathiyakumari; V, Preamsudha
    The availability of large quantity of text documents from the World Wide Web and business document management systems has made the dynamic separation of texts into new categories as a very important task for every business intelligence systems. Text document clustering is one of the emerging and most needed clustering techniques used to cluster documents with regard to similarity among documents. It is used widely in digital library management system in the modern context. Document clustering is widely applicable in areas such as search engines, web mining, information retrieval, and topological analysis. There are several clustering approaches available in the literature to cluster the document. But most of the existing clustering techniques suffer from a wide range of limitations. The existing clustering approaches face the issues like practical applicability, very less accuracy, more classification time etc. Thus a novel approach is needed for providing significant accuracy with less classification time. In recent times, inclusion of fuzzy logic in clustering provides better clustering results. One of the widely used fuzzy logic based clustering is Fuzzy C-Means (FCM) Clustering. In order to further improve the performance of clustering, this thesis uses Modified Fuzzy C-Means (MFCM) Clustering. The documents are ranked using Term Frequency–Inverse Document Frequency (TF–IDF) technique. From the experimental results, it can be observed that the proposed technique results in better clustering when compared to the FCM clustering technique.
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    A COMPARATIVE STUDY OF FUZZY MODELS IN DOCUMENT CLUSTERING
    (International Journal on Computer Science and Engineering, 2012) G, Manimekalai; K, Sathiyakumari; V, Preamsudha
    The availability of large quantity of text documents from the World Wide Web and business document management systems has made the dynamic separation of texts into new categories as a very important task for every business intelligence systems. Text document clustering is one of the emerging and most needed clustering techniques used to cluster documents with regard to similarity among documents. It is used widely in digital library management system in the modern context. Document clustering is widely applicable in areas such as search engines, web mining, information retrieval, and topological analysis. There are several clustering approaches available in the literature to cluster the document. But most of the existing clustering techniques suffer from a wide range of limitations. The existing clustering approaches face the issues like practical applicability, very less accuracy, more classification time etc. Thus a novel approach is needed for providing significant accuracy with less classification time. In recent times, inclusion of fuzzy logic in clustering provides better clustering results. One of the widely used fuzzy logic based clustering is Fuzzy C-Means (FCM) Clustering. In order to further improve the performance of clustering, this thesis uses Modified Fuzzy C-Means (MFCM) Clustering. The documents are ranked using Term Frequency–Inverse Document Frequency (TF–IDF) technique. From the experimental results, it can be observed that the proposed technique results in better clustering when compared to the FCM clustering technique
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    A COMPARATIVE STUDY OF MACHINE LEARNING ALGORITHMS APPLIED TO PREDICTIVE DIABETES DATA
    (CiiT International Journal of Data Mining Knowledge Engineering, 2009-11) C, Deepa; K, Sathiyakumari; V, Preamsudha
    Healthcare industry encompasses abundant data, which is increasing everyday. Conversely, tools for analyzing these records are incredibly less. Machine learning provides a lot of techniques for solving diagnostic problems in a variety of medical domains. Intelligent systems are able to learn from machine learning methods, when they are provided with a set of clinical cases as training set. This paper aims at a comparative study of widely used supervised classification algorithms – Naïve Bayes, Multi Layer Perceptrons, Logistic Model Trees, and Nearest Neighbor with Generalized Exemplars applied to predictive diabetes dataset. The machine learning algorithms used in this study are chosen for their representability and diversity. They are evaluated on the basis of their accuracy, learning time and error rates.
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    A SURVEY ON VARIOUS APPROACHES IN DOCUMENT CLUSTERING
    (International Journal of Computer Technology and Applications, 2011) G, Manimekalai; K, Sathiyakumari; V, Preamsudha
    Document clustering is the process of segmenting a particular collection of texts into subgroups including content based similar ones. The purpose of document clustering is to meet human interests in information searching and understanding. Nowadays all paper documents are in electronic form, because of quick access and smaller storage. So, it is a major issue to retrieve relevant documents from the larger database. Text mining is not a standalone task that human analysts typically engage in. The goal is to transform text composed of everyday language in a structured, database format. In this way, heterogeneous documents are summarized and presented in a uniform manner. Among others, the challenging problems of document clustering are big volume, high dimensionality and complex semantics
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    UNSUPERVISED APPROACH FOR DOCUMENT CLUSTERING USING MODIFIED FUZZY C MEAN ALGORITHM
    (International Journal of Computer & Organization Trends, 2011) G, Manimekalai; V, Preamsudha; K, Sathiyakumari
    Clustering is one the main area in data mining literature. There are various algorithms for clustering. There are several clustering approaches available in the literature to cluster the document. But most of the existing clustering techniques suffer from a wide range of limitations. The existing clustering approaches face the issues like practical applicability, very less accuracy, more classification time etc. In recent times, inclusion of fuzzy logic in clustering results in better clustering results. One of the widely used fuzzy logic based clustering is Fuzzy C-Means (FCM) Clustering. In order to further improve the performance of clustering, this thesis uses Modified Fuzzy C-Means (MFCM) Clustering. Before clustering, the documents are ranked using Term Frequency–Inverse Document Frequency (TF–IDF) technique. From the experimental results, it can be observed that the proposed technique results in better clustering results when compared to the existing technique