The process of finding out valuable knowledge drawn out from web data is known as web mining. Identifying the various patterns and utilizing the vast knowledge extracted from those patterns is important from various perspectives such as business intelligence, e-learning, personalization etc. The web mining area which deals with extraction of patterns from user’s weblogs is called as web usage mining, which is an implementation part of data mining. This paper focuses on the working of web usage mining, data sources for web usage mining and applications of web usage mining is explained in detail in this paper. Further, we explain the issues and current challenges in web usage mining.

INTRODUCTION

With the advent of various technologies, social networking sites, e-business site, e-learning sites etc. Every second new data is generated. The data growth rate is exponential. On a single tweet, for example, one can find millions of messages or retweets on Twitter. All this is possible because of the internet. Thus, The World Wide Web is a large source of varied data, a collection of billion documents, which either comes from the content in the web pages or from the various hyperlinks or structure of the websites i.e. web structure or from the log files depicting the web usage. The data or information is collected from various points or data sources. The Internet is also a form of network and in a network data is available at various nodes. Here, nodes can represent servers, client machines, intermediate devices popularly called as proxy servers or various databases that are stored on machines. Web mining is the part of data mining from which we derive meaningful and useful knowledge from text or contents present in web pages, hyperlinks and user usage logs [2]. For a clearer understanding, web mining has 3 parts: Web Structure Mining, Web Content Mining, and Web Usage Mining. The Web Content mining deals with the raw data available in the web pages; the source data mainly consists of the textual information, images, graphic, audio etc. present in the web documents. Since web content mining is basically related to the web content, the main application fields of web content are a content-based ranking of web pages and contentbased categorization [3]. The main focal point of Web Structure mining is the structure of websites. The way in which a web page is arranged is outlined by the structure data. There are two types of structure information: Inter-page structure information and Intra-page structure information. The intra-page structure information is illustrated by a treelike structure which is composed of HTML and XML tags. [3,9] whereas the hyperlinks that associate one page with another is inter-page structure information. With the information obtained from web structure mining, link-based categorization of web pages is done; this is an application of web structure mining. Web Usage Mining is the portion that focuses on extraction on knowledge from log files and helps in finding user behavior [13,15]; sources for these log files is servers, proxy servers or clients which can collect the typical information such as IP address, page references, access time etc. which are represented into standard formats and major application areas are web personalization, Business intelligence, E-commerce, E-learning etc. The paper is detailed as Section I defines web mining and its types; Section II illustrates web usage mining, Section III discuss the process of Web Usage Mining and the various data sources. Section IV describes the different techniques used in the process of web usage mining. Section V discusses the application areas in detail. Section VI discusses the trending issues and challenges faced in this field. Section VII discusses the future trends in web usage mining.