Informatics Publishing Limited

Abstract

Similar to data mining, three major web mining operations include clustering, association rule mining, and sequential analysis. Typical clustering operations in web mining involve finding natural groupings of web resources or web users. Researchers have found and pointed at some important and fundamental differences between clustering in conventional applications and clustering in web mining. Moreover, due to variety of reasons inherent in web browsing and web logging, the likelihood of bad and incomplete data is higher. This is where Rough Set Theory can play a crucial role and researchers have been utilizing this in clustering the incomplete data and thus aiding in decision making. This paper aims at understanding the Rough Set Theory and its applications in web mining.

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Abstract

Data compression is a key component for data storage systems and for communication purposes. Lempel-Ziv-Welch (LZW) data compression algorithm is popular for data compression because it is an adaptive algorithm and achieves an excellent compromise between compression performance and speed of execution. LZW is a dictionary based data compression algorithm, which compress the data in a lossless manner so that no information is lost. But LZW algorithm fails in case of small amount of data. In this case it expands the data instead of compressing it.  In this paper a system is proposed to achieve high compression even if data file contains small amount of data.

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