Using clustering to improve WLZ77 compression

Many types of Information Retrieval Systems (IRS) are created and more and more documents are stored in them too. The fundamental process of IRS is building of textual database, and compression of the documents stored in the database. One possibility for compression of textual data is word-based compression. Several algorithms for word-based compression algorithms based on Huffman encoding, LZW or BWT algorithm was proposed. In this paper, we describe word-based compression method based on LZ77 algorithm. IRS can also perform cluster analysis of textual database to improve quality of answers to users’ queries. The information retrieved from the clustering can be very helpful in compression. Word-based compression using information about cluster hierarchy is presented in this paper. Experimental results which are provided at the end of the paper were achieved not only using well-known word-based compression algorithms WBW and WLZW but also using quite new WLZ77 algorithm.