International Research Journal of Engineering and Technology (IRJET) e-ISSN: 2395 -0056 Volume: 04 Issue: 1 | January -2017 www.irjet.net p-ISSN: 2395-0072 © 2017, IRJET | Impact Factor value: 4.45 | ISO 9001:2008 Certified Journal | Page 502 Classification of User & Pattern discovery in WUM: A Survey Shahu P.S. 1 , Jamthe D.V. 2 , Nikose A. A. 3 1 M.Tech Student 1 Dept. of CSE, PBCOE, Maharashtra, India 2 Asst. Prof Dept. of CSE, PBCOE, Maharashtra, India 3 Asst. Prof Dept. of CSE , PBCOE, Maharashtra ,India ---------------------------------------------------------------------***--------------------------------------------------------------------- Abstract - Web usage mining effectively and efficiently serves the needs of the user visiting the websites. Web usage mining is based upon the discovery and analysis of web usage patterns from web logs. The purpose of this survey to identify the user’s classifications based on discovery pattern from web logs. The work is done in three steps. Preprocessing is done in first step to remove useless data from web log file that reduced its size. In next step, for discovering usage pattern cleaned log file is uses. In last step discovered pattern leads to the classification of user. Classification of users is identified on the basis of countries, direct entry to the site or referred by the other site, time of access, user and IP address based. All the identifies classification of user can be used by the administrator for the effective administration and website personalization. That result into fulfill the specific needs of specific community users and hence the profit can be increased. Key Words: Web mining, Web usages mining, Pattern discovery, SVM. 1. INTRODUCTION Web mining is the process of examining data sets collected from various sources methodically and in detail, in order interprets it to get useful information. These data sets may consist of web log data. Researchers have classified web mining into 3 types, namely, web structure, content and usage mining. This classification is based on the type of data to be mined [5]. Fig 1: Classification of Web Mining Web usage mining is also termed as web log mining. It is based on discovery and analysis of web usage pattern from web logs. It includes web server log, proxy server log, web browser logs, etc. and the logs are created when users communicate with the web server. The web logs facilitate web administrator to identify the users, their location and browsing pattern etc. It allows identifying website. It also stores the information such as IP address, referring website, timestamp, browser used, used platform etc. The interesting information collected from this web log helps website administrator serving effectively the needs of the users visiting their website efficiently. Web usage mining focuses on two points- 1) how the website administrators want their website to be used by the user and 2) how the user actually uses this websites. The deviation between the actual and expected use can be reduce after recognizing and personalizing website according the actual need of user. The main focus of this paper is to classify the user in the basis of discover patterns from web usage log which gets created when user interact with the web server. 2. LITERATURE SURVEY Satya Prakash Singh, Meenu [1] discussed different tools and techniques for web usage mining. They show how Patterns are discovered by making various techniques like statistical analysis, association rule, clustering, classification, sequential pattern. They include of knowledge query mechanism and intelligent agent improvement the efficiency of pattern analysis. J. Umarani, K. Karpagam [2] they mainly concentrates on methods applied in user identification phase of data preprocessing and prepared analysis of these methodologies to find out the more appropriate on web server log. K.Dharmarajan , M. A. Dorairangaswamy [3] author describe how to discover patterns from browsing and navigation data of web users. They mainly focus on extraction of user frequent access page using web log data. This complete analysis work has been implemented in the Web Log Expert tool.