Evaluation of Partition-Based Text Clustering Techniques to Categorize Indic Language Documents
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Wide availability of electronic data has led to the vast interest in text analysis, information retrieval and text categorization methods. To provide a better service, there is a need for non-English based document analysis and categorizing systems, as is currently available for English text documents. This study is mainly focused on categorizing Indic language documents. The main techniques examined in this study include data pre-processing and document clustering. The approach makes use of a transformation based on the text frequency and the inverse document frequency, which enhances the clustering performance. This approach is based on latent semantic analysis, k-means clustering and Gaussian mixture model clustering. A text corpus categorized by human readers is utilized to test the validity of the suggested approach. The technique introduced in this work enables the processing of text documents written in Sinhala, and empowers citizens and organizations to do their daily work efficiently.
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