{'fraction_non_alphanumeric': 0.021480804387568556, 'fraction_numerical': 0.0013711151736745886, 'mean_word_length': 4.715404699738904, 'pattern_counts': {'":': 0, '<': 0, '<?xml version=': 0, '>': 0, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
Efficient Frequent Pattern Mining in Web Log

Semantic Web Mining aims at combining the two fast- developing research areas Semantic Web and Web Mining. The idea is to improve, on the one hand, the results of Web Mining by exploiting the new semantic structures in the Web; and to make use of Web Mining, on the other hand, for building up the Semantic Web. Frequent pattern mining is an important research area in the field of web data mining with wide range of applications. One of them is to use frequent pattern discovery methods in Web log data. The aim of discovering frequent patterns in Web log data is to obtain information about the navigational behaviour of the users. In this paper we give road map of semantic web and web mining and we present P and T tree based pattern mining approaches for the Web usage mining. Our experiment shows that P and T tree based pattern mining approaches is give better performance than existing one. The massive uses of the Internet have made automatic knowledge extraction from web log files a necessity. Information providers are interested in techniques that could learn web user's information needs and preferences. This can be used to improve the effectiveness of their web sites by adapting the information structure of the sites to the users' behaviour (1). However, it is hard to find appropriate tools for analysing raw web log data to retrieve significant and useful information. Currently, there are several generic web log analysis tools but most of them are disliked by their users and considered too slow, inflexible, expensive, difficult to maintain or very limited in the results they can provide. Recently, the advent of data mining techniques (4) for discovering usage patterns from Web data (a.k.a. web log mining or web usage mining) indicates that these techniques can be a viable alternative to traditional decision-making tools. Web usage mining is the application of data mining techniques to discover usage patterns from Web data, in order to understand and better serve the needs of Web-based applications. Web usage mining consists of three phases, namely pre-processing, pattern discovery, and pattern analysis (5).