A Novel Incremental Mining Algorithm of Frequent Patterns for Web Usage Mining

Because data warehouse is frequently changing, incremental data leads to old knowledge which is mined formerly unavailable. In order to maintain the discovered knowledge and patterns dynamically, this study presents a novel algorithm updating for global frequent patterns-IPARUC. A rapid clustering method is introduced to divide database into n parts in IPARUC firstly, where the data are similar in the same part. Then, the nodes in the tree are adjusted dynamically in inserting process by ＂pruning and laying back＂ to keep the frequency descending order so that they can be shared to approaching optimization. Finally local frequent itemsets mined from each local dataset are merged into global frequent itemsets. The results of experimental study are very encouraging. It is obvious from experiment that IPARUC is more effective and efficient than other two contrastive methods. Furthermore, there is significant application potential to a prototype of Web log Analyzer in web usage mining that can help us to discover useful knowledge effectively, even help managers making decision.

Semantic Session Analysis for Web Usage Mining

A semantic session analysis method partitioning Web usage logs is presented. Semantic Web usage log preparation model enhances usage logs with semantic. The Markov chain model based on ontology semantic measurement is used to identifying which active session a request should belong to. The competitive method is applied to determine the end of the sessions. Compared with other algorithms, more successful sessions are additionally detected by semantic outlier analysis.

Incremental Web Usage Mining Based on Active Ant Colony Clustering

To alleviate the scalability problem caused by the increasing Web using and changing users＇ interests, this paper presents a novel Web Usage Mining algorithm-Incremental Web Usage Mining algorithm based on Active Ant Colony Clustering. Firstly, an active movement strategy about direction selection and speed, different with the positive strategy employed by other Ant Colony Clustering algorithms, is proposed to construct an Active Ant Colony Clustering algorithm, which avoid the idle and ＂flying over the plane＂ moving phenomenon, effectively improve the quality and speed of clustering on large dataset. Then a mechanism of decomposing clusters based on above methods is introduced to form new clusters when users＇ interests change. Empirical studies on a real Web dataset show the active ant colony clustering algorithm has better performance than the previous algorithms, and the incremental approach based on the proposed mechanism can efficiently implement incremental Web usage mining.

The Study on Network Education based on Web Data Mining

Since the emergency of the mining of web usage patterns in the nineties of the 20th century, it has gotten a great development because of its wide range of application. To take advantage of the mining of web usage patterns, it will make network education system to meet personalized requirement better by distinguishing user interest and finding out important page.

Matrix dimensionality reduction for mining typical user profiles

Recently clustering techniques have been used to automatically discover typical user profiles. In general, it is a challenging problem to design effective similarity measure between the session vectors which are usually high-dimensional and sparse. Two approaches for mining typical user profiles, based on matrix dimensionality reduction, are presented. In these approaches, non-negative matrix factorization is applied to reduce dimensionality of the session-URL matrix, and the projecting vectors of the user-session vectors are clustered into typical user-session profiles using the spherical k -means algorithm. The results show that two algorithms are successful in mining many typical user profiles in the user sessions.

Web Fuzzy Clustering and a Case Study

We combine the web usage mining and fuzzy clustering and give the concept of web fuzzy clustering, and then put forward the web fuzzy clustering processing model which is discussed in detail. Web fuzzy clustering can be used in the web users clustering and web pages clustering. In the end, a case study is given and the result has proved the feasibility of using web fuzzy clustering in web pages clustering. Key words web mining - web usage mining - web fuzzy clustering - WFCM CLC number TP 391 Foundation item: Supported by the National Natural Science Foundation of China (90104005)Biography: LIU Mao-fu (1977-), male, Ph. D candidate, research direction: artificial intelligence, web mining, image mining.

Similarity Measurement of Web Sessions Based on Sequence Alignment

The task of clustering Web sessions is to group Web sessions based on similarity and consists of maximizing the intra-group similarity while minimizing the inter-group similarity. The first and foremost question needed to be considered in clustering Web sessions is how to measure the similarity between Web sessions. However, there are many shortcomings in traditional measurements. This paper introduces a new method for measuring similarities between Web pages that takes into account not only the URL but also the viewing time of the visited Web page. Then we give a new method to measure the similarity of Web sessions using sequence alignment and the similarity of Web page access in detail Experiments have proved that our method is valid and efficient.

Conceptualizing Mining of Firm's Web Log Files

In this era of a data-driven society, useful data(Big Data) is often unintentionally ignored due to lack of convenient tools and expensive software. For example, web log files can be used to identify explicit information of browsing patterns when users access web sites. Some hidden information,however, cannot be directly derived from the log files. We may need external resources to discover more knowledge from browsing patterns. The purpose of this study is to investigate the application of web usage mining based on web log files. The outcome of this study sets further directions of this investigation on what and how implicit information embedded in log files can be efficiently and effectively extracted. Further work involves combining the use of social media data to improve business decision quality.

Parallelized User Clicks Recognition from Massive HTTP Data Based on Dependency Graph Model

With increasingly complex website structure and continuously advancing web technologies,accurate user clicks recognition from massive HTTP data,which is critical for web usage mining,becomes more difficult.In this paper,we propose a dependency graph model to describe the relationships between web requests.Based on this model,we design and implement a heuristic parallel algorithm to distinguish user clicks with the assistance of cloud computing technology.We evaluate the proposed algorithm with real massive data.The size of the dataset collected from a mobile core network is 228.7GB.It covers more than three million users.The experiment results demonstrate that the proposed algorithm can achieve higher accuracy than previous methods.

An Ant Colony Approach for Users Navigation Patterns Mining

In the advance of E-commerce, the importance of predicting the next request of a user as he or she visits Web pages grows larger than before. Web usage mining is the process of applying data mining to the discovery of user behavior patterns based on Web log data, well suited to this problem. As an important field of Web usage mining, mining user navigation patterns is the fundamental approach for generating recommendations. In this paper, we propose an ant colony approach for navigation patterns. We use the ant theory as a metaphor to guide user＇s choice in the Web site.