Business Intelligence from Web Usage Mining

The rapid e-commerce growth has made both business community and customers face a new situation. Due to intense competition on one hand and the customer's option to choose from several alternatives business community has realized the necessity of intelligent marketing strategies and relationship management. Web usage mining attempts to discover useful knowledge from the secondary data obtained from the interactions of the users with the Web. Web usage mining has become very critical for effective Web site management, creating adaptive Web sites, business and support services, personalization, network traffic flow analysis and so on. In this paper, we present the important concepts of Web usage mining and its various practical applications. We further present a novel approach 'intelligent-miner' (i-Miner) to optimize the concurrent architecture of a fuzzy clustering algorithm (to discover web data clusters) and a fuzzy inference system to analyze the Web site visitor trends. A hybrid evolutionary fuzzy clustering algorithm is proposed in this paper to optimally segregate similar user interests. The clustered data is then used to analyze the trends using a Takagi-Sugeno fuzzy inference system learned using a combination of evolutionary algorithm and neural network learning. Proposed approach is compared with self-organizing maps (to discover patterns) and several function approximation techniques like neural networks, linear genetic programming and Takagi-Sugeno fuzzy inference system (to analyze the clusters). The results are graphically illustrated and the practical significance is discussed in detail. Empirical results clearly show that the proposed Web usage-mining framework is efficient

cANT WUM : WEB USERS CLASSIFICATION USING ANT COLONY OPTIMIZATION ALGORITHM

Web Usage Mining (WUM) is the use of data mining methods to extract knowledge from web usage data. One function of WUM is to support Business Intelligence (BI) purpose in which one of the important information needed is the classification of web users that can be used for acquisition, penetration, and user retention activity. There are two main problems encountered in conducting the classification of web users. The first is the determination of antecedent attributes as a term of classification rules, which is a major problem in data mining classification function in general. The second problem is the preprocessing activity which involves preparing the supporting data for the web users’ classification need which is the most difficult stage in WUM. For the web user classification method, we propose a classification method based on ant colony optimization method (ACO) as a distributed intelligent system using heuristic function which is in line with the problem areas. We proposed a heuristic functions for web user classification based on web usage data that uses entropy of antecedent candidate, information gain from attribute of total number of web user access  and average of access duration of web user.  For preprocessing purpose, a method of data preparation that can support the needs of web users’ classification is proposed. The data used consists of web access log data, web user profile data and web transaction data. The preprocessing activity consists of parsing, data cleansing, and extraction of the web user sessions using heuristic method concerning web page access timeout and differences in web browser agent. Testing is done by comparing the performance of the proposed algorithm with Ant-Miner algorithm, cAnt-Miner algorithm, and the Continuous Ant-Miner algorithm. The results of testing of four web data shows that the performance of the proposed algorithm is better in terms of accuracy of rules and simplification of rules

Examining Competitive Intelligence Using External and Internal Data Sources: A Text Mining Approach

Competitive intelligence (CI) is the practice of studying competitors and competitive environment in support of firm’s strategic decision-making process. Currently, competitors are usually studied from business profile information and reports edited by CI professionals. While being inefficient and expensive in labor and resources, their results are often incomplete and lack objectivity. Some existing literatures introduced text mining to leverage Web information for CI usage. Despite improving on coverage, most of these analyses identify competitors using name co-occurrences from a single data source. The validity and reliability of these studies remain questionable. Our experiment demonstrates that syntactic level text mining can lead to improvements on CI performance. It also shows that the selection of different online data sources and competitor name extraction methods have different implications on CI outcome

Mining User Interests from User Search by Using Web Log Data

Web Usage Mining (WUM) is a kind of data mining method that can be used to discover user access patterns from Web log data. A lot of work has been done already about this area and the obtained results are used in different applications such as recommending the Web usage patterns, personalization, system improvement and business intelligence. WUM includes three phases that are called preprocessing, pattern discovery and pattern analysis. There square measure totally different techniques for WUM that have their own benefits and downsides. We tend to initial describe a way for extracting a worldwide linguistics illustration of a pursuit question log then show, however, we are able to use it to semantically extract the user interests. During this paper extraction of users interest from journal knowledge will be done, that square measure supported visit time and visit density which might be get from an analysis of internet users journal knowledge

Datamining for Web-Enabled Electronic Business Applications

Web-Enabled Electronic Business is generating massive amount of data on customer purchases, browsing patterns, usage times and preferences at an increasing rate. Data mining techniques can be applied to all the data being collected for obtaining useful information. This chapter attempts to present issues associated with data mining for web-enabled electronic-business

Binary Particle Swarm Optimization based Biclustering of Web usage Data

Web mining is the nontrivial process to discover valid, novel, potentially useful knowledge from web data using the data mining techniques or methods. It may give information that is useful for improving the services offered by web portals and information access and retrieval tools. With the rapid development of biclustering, more researchers have applied the biclustering technique to different fields in recent years. When biclustering approach is applied to the web usage data it automatically captures the hidden browsing patterns from it in the form of biclusters. In this work, swarm intelligent technique is combined with biclustering approach to propose an algorithm called Binary Particle Swarm Optimization (BPSO) based Biclustering for Web Usage Data. The main objective of this algorithm is to retrieve the global optimal bicluster from the web usage data. These biclusters contain relationships between web users and web pages which are useful for the E-Commerce applications like web advertising and marketing. Experiments are conducted on real dataset to prove the efficiency of the proposed algorithms

Competitive Intelligence and Internet Sources

In the Knowledge Age to maintain profitability and in some cases to remain in the market, companies must focus their actions in activities such as collecting, filtering, and disseminating information about market, about competitors and their actions. Those are part of Competitive Intelligence (CI) concept. In digital age, most of the information needed for CI projects is available on the web. This paper focuses on this field and presents a mix of directions that companies need to take into consideration in their CI projects in order to achieve the goals.competitive intelligence, web mining, information