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In recent years, it has been witnessed that the ever-interesting and upcoming publishing medium is the World Wide Web. Much of the web content is unstructured so gathering and making sense of such data is very tedious. Web servers worldwide generate a vast amount of information on web users' browsing activities. Several researchers have studied these so-called web access log data to better understand and characterize web users. Data can be enriched with information about the content of visited pages and the origin (e.g., geographic, organizational) of the requests. The goal of this project is to analyze user behavior by mining enriched web access log data. The several web usage mining methods for extracting useful features is discussed and employ all these techniques to cluster the users of the domain to study their behaviors comprehensively. The contributions of this thesis are a data enrichment that is content and origin based and a treelike visualization of frequent navigational sequences. This visualization allows for an easily interpretable tree-like view of patterns with highlighted relevant information. The results of this project can be applied on diverse purposes, including marketing, web content advising, (re-)structuring of web sites and several other E-business processes, like recommendation and advertiser systems. It also rank the best relevant documents based on Top K query for effective and efficient data retrieval system. It filters the web documents by providing the relevant content in the search engine result page (SERP).Index Terms-SERP, Top K-query, World Wide Web, Data-based approach, Web mining. Introduction:

Bots have made an appearance on social media in a variety of ways. Twitter, for instance, has been particularly hard hit, with bots accounting for a shockingly large number of its users. These bots are used for nefarious purposes such as disseminating false information about politicians and inflating celebrity expectations. Furthermore, these bots have the potential to skew the results of conventional social media research. With the multiple increases in the size, speed, and style of user knowledge in online social networks, new methods of grouping and evaluating such massive knowledge are being explored. Getting rid of malicious social bots from a social media site is crucial. The most widely used methods for identifying fraudulent social bots focus on the quantitative measures of their actions. Social bots simply mimic these choices, leading to a low level of study accuracy. Transformation clickstream sequences and semi-supervised clustering were used to develop a new technique for detecting malicious social bots. This method considers not only the probability of user activity clickstreams being moved, but also the behavior's time characteristic. The detection accuracy for various kinds of malware social bots by the detection technique assisted transfer probability of user activity clickstreams will increase by a mean of 12.8 percent, as per results from our research on real online social network sites, compared to the detection method funded estimate of user behaviour.