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A methodology for automatically identifying and clustering semantic features or topics in a heterogeneous text collection is presented. Textual data is encoded using a low rank nonnegative matrix factorization algorithm to retain natural data nonneg-ativity, thereby eliminating the need to use subtractive basis vector and encoding calculations present in(More)
8 Abstract 9 A methodology for automatically identifying and clustering semantic features or topics in a heterogeneous text col-10 lection is presented. Textual data is encoded using a low rank nonnegative matrix factorization algorithm to retain nat-11 ural data nonnegativity, thereby eliminating the need to use subtractive basis vector and encoding(More)
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