An efficient text compression algorithm - data mining perspective

Abstract

The paper explores a novel compression perspective of Data Mining. Frequent Pattern Mining, an important phase of Association Rule Mining is employed in the process of Huffman Encoding for Lossless Text Compression. Conventional Apriori algorithm has been refined to employ efficient pruning strategies to optimize the number of pattern(s) employed in encoding. Detailed simulations of the proposed algorithms in relation to Conventional Huffman Encoding has been done over benchmark datasets and results indicate significant gains in compression ratio.