Effective web log mining and online navigational pattern prediction: a Survey

The web has become the world's largest repository of knowledge. Web usage mining is the process of discovering knowledge from the interactions generated by the user in the form of access logs, cookies, and user sessions data. Web Mining consists of three different categories, namely Web Content Mining, Web Structure Mining, and Web Usage Mining (is the process of discovering knowledge from the interaction generated by the users in the form of access logs, browser logs, proxy-server logs, user session data, cookies). Accurate web log mining results and efficient online navigational pattern prediction are undeniably crucial for tuning up websites and consequently helping in visitors' retention. Like any other data mining task, web log mining starts with data cleaning and preparation and it ends up discovering some hidden knowledge which cannot be extracted using conventional methods. After applying web mining on web sessions we will get navigation patterns which are important for web users such that appropriate actions can be adopted. Due to huge data in web, discovery of patterns and there analysis for further improvement in website becomes a real time necessity. The main focus of this paper is using of hybrid prediction engine to classify users on the basis of discovered patterns from web logs. Our proposed framework is to overcome the problem arise due to using of any single algorithm, we will give results based on comparison of two different algorithms like Longest Common Sequence (LCS) algorithm and Frequent Pattern (Growth) algorithm. As daily use of World Wide Web is increasing, mining of the database is having more demanding. The database of web is in the form of web sessions with session id or session owner. So for this type of mining is called Web Mining. And in the Web Mining when we use to find path traversal pattern for decision management in website design, then it comes under the web usage Mining (12). Web usage mining is also called web log mining. It is a web mining technique which is based upon the discovery and analysis of web usage patterns from web logs. These web logs include web server logs, proxy server logs, web browser logs, etc., and are created when users communicate with the web server (13). (1)Web usage mining is the process of finding out what users are looking for on the internet. Few users might be looking at only documented data, whereas some others might be interested in multimedia data. It is the submission of facts and figures mining techniques to find out interesting usage patterns from World Wide Web facts and figures in alignment to realize and better serve the desires of Web based applications. (2)A new session in LCS is classified into any one of the clusters and prediction list is generated based on the navigation patterns of corresponding cluster. A prediction model by considering order information of pages and time spent on them in a session. (3) Data used for Web usage mining, can be collected at one of these three parts: Server level, Client Level, and proxy level collection. The input for the Web Usage Mining procedure is a client session document, which is fundamentally a pre-processed record and comprises of information, for example, who visited the site and what pages were visited and for to what extent, with their respective order. However it may contain unnecessary information. This unnecessary information can be minimized or reduced by data pre-processing of the web log data. After data cleaning the unique number of user and session identify and at last stage of data preprocessing we get the frequent user's access pattern from the web server access log file data. (4)A particular user or a set of user taking advantage of knowledge gained from user's navigational pattern and interest of the individual user with the conjunction of content and structure of Web Sites. (5) Web usage mining involves the automatic detection of user access patterns on one or more web servers. It is an application of data mining algorithms to web access logs to find the trends and regularities in web users' navigation patterns. There are many kinds of data that can be used in web mining