Web Log Preprocessing Research Articles

Web page recommendation system using bat optimization and weighted support vector machine algorithm for health care service

Web-page recommendation commands a predominant part in smartweb systems. Discovery of meaningfulinformation from web utilization data and improvedrepresentation of knowledge for resourcefulweb-page recommendations are important and a huge challenge. The availabletechniques faces problems with information over load when the users tryretrieving the exact information that end users require. In addition,it also experiences issues with meaningful semantic information and thereforeit does not yield satisfactory web based services for the end users. To deal with the above statedissues, in this research work, BAOs (Bat Agent Optimizations) and WSVMs (Weighted Support Vector Machines) are combined to form BAO+WSVM algorithm for web page recommendations that are effective and useful. The experiments are carried out on the health records of COPDs (chronic obstructive pulmonary diseases) collected from websites of government hospitals. Attention has been paid on BAO+WSVM to providetheoptimal webpages for patients. User bat agent forms the interface between the user and the system providing an active environment for the users. At first, web log pre-processing is carried out with the help of data cleaning, user identification, and session identification. Later, Semantic information extractions are carried out applying DOWLs (Domain OntologyWeb language) which helps construct the ontology associated with a website utilizing its structure.

Web Log Pre-processing and Analysis for Generation of Learning Profiles in Adaptive E-learning

Adaptive E-learning Systems (AESs) enhance the efficiency of online courses in education by providing personalized contents and user interfaces that changes according to learner’s requirements and usage patterns. This paper presents the approach to generate learning profile of each learner which he

Web Log Pre-processing and Analysis for Generation of Learning Profiles in Adaptive E-learning

Adaptive E-learning Systems (AESs) enhance the efficiency of online courses in education by providing personalized contents and user interfaces that changes according to learner’s requirements and usage patterns. This paper presents the approach to generate learning profile of each learner which helps to identify the learning styles and provide Adaptive User Interface which includes adaptive learning components and learning material. The proposed method analyzes the captured web usage data to identify the learning profile of the learners. The learning profiles are identified by an algorithmic approach that is based on the frequency of accessing the materials and the time spent on the various learning components on the portal. The captured log data is pre-processed and converted into standard XML format to generate learners sequence data corresponding to the different sessions and time spent. The learning style model adopted in this approach is Felder-Silverman Learning Style Model (FSLSM). This paper also presents the analysis of learner’s activities, preprocessed XML files and generated sequences.

Discovery of web usage patterns using fuzzy mountain clustering

Analysis of web server logs of e-business organisations is critical to provide insight into users' web usage behaviour which can assist in designing most attractive websites. In this article, a mountain density function MDF-based fuzzy clustering framework to discover user session clusters from web logs is proposed. Major steps in this framework include web log preprocessing, MDF-based discovery of user session clusters and their validation. To deal with high dimensionality of user sessions, a fuzzy approach for assigning weights to user sessions has been proposed. For the discovery of user session clusters, fuzzy c-means FCM and fuzzy c-medoids FCMed algorithms are explored. Since the selection of suitable initial cluster centres is a big challenge, MDF-based fuzzy c-means MDFCM and fuzzy c-medoids MDFCMed algorithms are proposed to overcome this problem. Our results show that quality of clusters formed using MDFCM/MDFCMed is much better than FCM and FCMed.

Knowledge Discovery from Web Usage Data: An Efficient Implementation of Web Log Preprocessing Techniques

Web Usage Mining (WUM) refers to extraction of knowledge from the web log data by application of data mining techniques. WUM generally consists of Web Log Preprocessing, Web Log Knowledge Discovery and Web Log Pattern Analysis. Web Log Preprocessing is a major and complex task of WUM. Elimination of noise and irrelevant data, thereby reducing the burden on the system leads to efficient discovery of patterns by further stages of WUM. In this paper, Web Log Preprocessing Methods to efficiently identify users and user sessions have been implemented and results have been analyzed.

Mountain density-based fuzzy approach for discovering web usage clusters from web log data

Due to the continuous proliferation of e-businesses, there is intense competition among organizations to attract and retain customers. Analyses of the web server logs of these organizations are critical for obtaining insights into web usage behavior, which can support the design of more attractive web structures. In this study, we propose a mountain density function (MDF)-based fuzzy clustering framework for discovering user session clusters in web log data. The major steps in this framework include web log preprocessing, MDF-based discovery of fuzzy user session clusters, and validation of these clusters. To consider the high dimensionality of user session data, we propose a fuzzy approach for assigning weights to user sessions. Fuzzy c-means (FCM) and fuzzy c-medoids (FCMed) algorithms are used to cluster the user sessions. The selection of suitable initial cluster centers is a major challenge for these methods, so we propose MDF-based FCM (MDFCM) and FCMed (MDFCMed) algorithms to overcome this problem. MDF-based clustering is also used to estimate the number of clusters. Our results clearly indicate that the quality of the clusters formed using the proposed algorithms is much better in terms of various validity measures compared with the FCM and FCMed algorithms.

A Neoteric Web Recommender System based on Approach of Mining Frequent Sequential Pattern from Customized Web Log Preprocessing

A Neoteric Web Recommender System based on Approach of Mining Frequent Sequential Pattern from Customized Web Log Preprocessing

Web Usage Association Rule Mining System

Introduction Web usage log files generated on web servers contain huge amount of information suitable for applying data mining methods to discover potentially useful knowledge (Kosala & Blockeel, 2000; Wang, Li, & Zhang, 2005). Discovering web usage association rules is one of the popular data mining methods that can be applied on the web usage log data. The information contained in association rules can be used to learn about website visitor behaviour patterns, enhance website structure making it more effective for the visitors, or improve web marketing campaigns (Anand, Mulvenna, & Chavielier, 2004; Cooley, Mobasher, & Srivastava, 1997). Originally, association rule mining algorithms were applied to the analysis of transactional databases (Agrawal, Imielinski, & Swami, 1993; Brin, Motwani, Ullman, & Tsur, 1997). When evaluating the association rule interestingness, various measures can be used to help find the rules that give maximally useful information to the user (Geng & Hamilton, 2006; Tan, Kumar, & Srivastava, 2004). Some of the proposed association rule interestingness measures are all-confidence (Omiecinski, 2003), collective strength (Aggarwal & Yu, 1998), conviction and lift (Brin et al., 1997). While association rule finding algorithms are complete in that they find all rules that satisfy defined constraints, they often result in a huge set of rules that is difficult to exploit in order to find those rules that are truly interesting to the user (Liu, Hsu, & Ma, 1999). This problem is aggravated in association rule mining of the web usage log data (Huang, Cercone, & Aijun, 2002). Web usage data is specific and differs from the market basket data in the way that it contains a large number of tightly correlated items (web resources or web pages) due to the link structure of a website. Web pages that are tightly linked together often occur in the same visitor sessions, which is a reason that the generated set of association rules contains a huge number of so called hard association rules, which are not truly interesting to a user (Huang, 2007). There are freely available data mining software systems that can be used for discovering association rules in the web log usage data (Weka 3, n.d.). Weka is a great data mining tool with a wide range of features (Witten, Frank, & Hall, 2011). We used Weka in our previous research (Dimitrijevic & Bosnjak, 2010), but ran into several obstacles. We found that Weka did not contain an integrated set of tools to support all phases of web usage mining. A special software had to be used for web log preprocessing, such as WUM Prep scripts. Further, we had to develop our own special purpose converters of the web log preprocessed data into the Weka's ARFF file format before running web usage association rule discovery algorithm in Weka. Other limitations of the current Weka version are that it offers four association rule interestingess measures without option of combining them. Weka can be extended to include more interestingness measures, but it would involve additional work. There is also no support for automatic pruning of the discovered association rules in Weka using the methods we proposed (this was confirmed to us by the Weka architect in a forum). We concluded that it would give us more flexibility in our research and ability to apply the methods we propose for pruning association rules and for combining various interestingness measures if we used our own independent tool specialized for Web Usage Mining. We present the first version of this tool in this paper. We apply the system for the discovery of the association rules on a real life data set and present the results using various parameter values. We propose a method to alleviate the problem of over-generation of not truly interesting rules in web usage mining by eliminating the rules that contain directly linked pages. …