Browsing by Author "Punitha, S C"

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    PARTITION DOCUMENT CLUSTERING USING ONTOLOGY APPROACH
    (IEEE, 2013-02-21) Punitha, S C; Jayasree, R; Punithavalli, M
    Data mining is the extraction of hidden predictive information from large databases and it is a powerful new technology with great potential to help companies focus on the most important information in their data warehouses. Data mining tools predict future trends and behaviors, allowing businesses to make proactive, knowledge-driven decisions. In data mining there are two activities such as Classification and clustering [5]. Text clustering typically involves clustering in a high dimensional space, which appears difficult with regard to virtually all practical settings. The creation and deployment of knowledge repositories for managing, sharing, and reusing tacit knowledge within an organization has emerged as a prevalent approach in current knowledge management practices.
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    PERFORMANCE EVALUATION OF SEMANTIC BASED AND ONTOLOGY BASED TEXT DOCUMENT CLUSTERING TECHNIQUES (Conference Paper)
    (Elsevier Ltd, 2012) Punitha, S C; Punithavalli, M
    The amount of digital information is created and used is steadily growing along with the development of sophisticated hardware and software. This has increased the need for powerful algorithms that can interpret and extract interesting knowledge from these data. Data mining is a technique that has been successfully exploited for this purpose. Text mining, a category of data mining, considers only digital documents or text. Text Clustering is the process of grouping text or documents such that the document in the same cluster are similar and are dissimilar from the one in other clusters. This paper studies the working of two sophisticated algorithms. The first work is a hybrid method that combines pattern recognition process with semantic driven methods for clustering documents, while the second uses an ontology-based approach to cluster documents. Through experiments, the performance of both the selected algorithms is analyzed in terms of clustering efficiency and speed of clustering.