  In this paper, a simple text categorization method using term-class relevance
measures is proposed. Initially, text documents are processed to extract
significant terms present in them. For every term extracted from a document, we
compute its importance in preserving the content of a class through a novel
term-weighting scheme known as Term_Class Relevance (TCR) measure proposed by
Guru and Suhil (2015) [1]. In this way, for every term, its relevance for all
the classes present in the corpus is computed and stored in the knowledgebase.
During testing, the terms present in the test document are extracted and the
term-class relevance of each term is obtained from the stored knowledgebase. To
achieve quick search of term weights, Btree indexing data structure has been
adapted. Finally, the class which receives maximum support in terms of
term-class relevance is decided to be the class of the given test document. The
proposed method works in logarithmic complexity in testing time and simple to
implement when compared to any other text categorization techniques available
in literature. The experiments conducted on various benchmarking datasets have
revealed that the performance of the proposed method is satisfactory and
encouraging.
