Web usage mining using rough agglomerative clustering

2014

Incremental growth in the use of web based services and systems have led to generation of such tremendous amounts of data which is beyond imagining. This data plays a vital role in determining the factors like user’s interests, priorities and product or services usage trends. This knowledge enables organizations to evaluate the effectiveness of their strategies and quality of services/products provided and leads them for further refinement. Solicitation of data mining approaches to process huge volume of data available on net is termed as web mining. Web usage is a further phase in web mining which discovers data about the use of internet. This paper aims at providing a review of phases and techniques involved in web usage mining.

2005

ABSTRACT The rising popularity of electronic commerce makes data mining an indispensable technology for several applications, especially online business competitiveness. The World Wide Web provides abundant raw data in the form of Web access logs. However, without data mining techniques, it is difficult to make any sense out of such massive data. In this chapter, we focus on the mining of Web access logs, commonly known as Web usage mining.

2016

Nowadays, web mining is the highest era to acquire information and knowledge. Web mining can be broadly defined as the search and measure of useful information from the World Wide Web. It provides automatic search of information resources available on-line, and the discovery of user access information from Web servers, i.e., Web usage mining. Web Usage Mining is the application of data mining methods which analyze the recordings of Web usage, which are stored in the form of Web server logs. The goal of web usage mining is to find the user's access patterns quickly in the form of frequent traversal paths, frequent access page-sets, and user clustering. Web usage mining mines the data which are generated by the web users while interacting with the web. The mining processes include data preparation, mining process, and process analysis. It provides automatic search of information resources available on-line, and the discovery of user access information from Web servers, i.e., Web u...

International Journal of Multimedia and Image Processing, 2011

There are many models in literature and practice that analyse user behaviour based on user navigation data and use clustering algorithms to characterize their access patterns. The navigation patterns identified are expected to capture the user's interests. In this paper, we model user behaviour as a vector of the time the user spends at each URL, and further classify a given new user access pattern. The clustering and classification methods of k-means with non-Euclidean similarity measure, Bayesian classifiers and artificial neural networks, with standardised fuzzy inputs are implemented and compared. Apart from identifying user behaviour, the model can also be used as a prediction system where we can identify deviational behaviour.

IJSRD, 2014

The World Wide Web contains an increasing amount of websites which in turn contains increasing number of web pages. When a user visits a new website he/she has to go through large number of web pages to meet their requirements. Web usage mining is the process of extracting useful knowledge from the server logs. This useful knowledge can be applied to target marketing and in the design of web portals. A Recommender system is one of the best web usage mining Application which reduces the difficulties faced by the users to meet their requirements .It recommends the pages of interest to the user. This survey paper includes the survey of different clustering and biclustering techniques. Also we will discuss the biclustering approach which has some advantages over the traditional clustering approach.

International Journal of Computer Theory and Engineering, 2016

Web mining is an exciting discipline in the domain of data mining as well as in classification/clustering. Identifying the usage patterns of users is very important in utilizing the vast information available in the World Wide Web. Web usage mining entails identifying usage pattern and has many practical applications. It focuses on techniques that have the potential to predict user behaviour while the user interacts with the Web. The aim of this paper is to provide a general view on web usage mining and its importance for designers and those interested in e-commerce and website personalization. The paper explains in detail the process of web usage mining and the different techniques used for pattern discovery. Also, it illustrates the different applications and tools used for web usage mining. Finally, it explains some current issues and challenges such as privacy and scalability, which are important issues in web usage mining..

2009

In the k means clustering algorithm right value of clusters (k) are initially unknown and effective selections of initial seed are also difficult. In this paper efficient k-means algorithm is proposed and implemented which overcome initial seed problem and unknown number of cluster problem. The algorithm is applied on real BIST server log data and Gaussian dataset to test its accuracy and efficiency. At application level this algorithm may used for efficient knowledge discovery from web repositories.

2017

An enormous amount of data is available in form of web documents over the World Wide Web and is increasing day by day. Web mining is used to extract useful information from web documents. Web mining is categorized into three types, namely, web content mining, web structure mining and web usage mining. Web usage mining is the data mining technique to mine the web log data from World Wide Web to extract useful information. Web usage mining useful for the applications like ecommerce to do personalized marketing, fight against terrorism, fraud detection, to identify criminal activities, web design etc. This paper is going to explain in detail about the process involved in Web Usage Mining, Web Usage Mining applications and tools. Keywords-Web Usage Mining, Web log files, Web usage mining process, Web usage mining applications and Tools.

2006

Discovering user access patterns from web access log is increasing the importance of information to build up adaptive web server according to the individual user's behavior. The variety of user behaviors on accessing information also grows, which has a great impact on the network utilization. In this paper, we present a rough set clustering to cluster web transactions from web access logs and using Markov model for next access prediction. Using this approach, users can effectively mine web log records to discover and predict access patterns. We perform experiments using real web trace logs collected from www.dusit.ac.th servers. In order to improve its prediction ration, the model includes a rough sets scheme in which search similarity measure to compute the similarity between two sequences using upper approximation.

T. Vijaya Kumar & H. S. Guruprasad, 2013

Web Usage Mining is the application of data mining techniques to learn usage patterns from Web server log file in order to understand and better serve the requirements of web based applications. Web Usage Mining includes three most important steps namely Data Preprocessing, Pattern discovery and Analysis of the discovered patterns. One of the most important tasks in Web usage mining is to find groups of users exhibiting similar browsing patterns. Grouping web transactions into clusters is important in order to understand user‟s navigational behavior. Different types of clustering algorithms such as partition based, distance based, density based, grid based, hierarchical and fuzzy clustering algorithms are used to find clusters from Web usage data. In this paper we propose an approach for clustering Web usage data based on Fuzzy tolerance rough set theory and table filling algorithm. First, we have constructed the sessions using concept hierarchy and link information. The similarity between two sessions is approximated by using Rough set tolerance relation. The the tolerance relation is reformulated into equivalence relation using fuzzy tolerance. Then the clusters are obtained by using modified table filling algorithm. We provide experimental results of Fuzzy rough set similarity and table filling algorithm on MSNBC web navigation data set. In this paper, we have considered the server log files of the Website www.enggresources.com for overall study and analysis.