Comparison of interestingness functions for learning web usage patterns
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Data Mining and Knowledge Discovery
Association Models for Web Mining
Data Mining and Knowledge Discovery
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IEEE Transactions on Knowledge and Data Engineering
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IEEE Transactions on Knowledge and Data Engineering
ICDE '95 Proceedings of the Eleventh International Conference on Data Engineering
Mining Access Patterns Efficiently from Web Logs
PADKK '00 Proceedings of the 4th Pacific-Asia Conference on Knowledge Discovery and Data Mining, Current Issues and New Applications
Data Mining of User Navigation Patterns
WEBKDD '99 Revised Papers from the International Workshop on Web Usage Analysis and User Profiling
Web Usage Mining as a Tool for Personalization: A Survey
User Modeling and User-Adapted Interaction
Efficient data mining for calling path patterns in GSM networks
Information Systems
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Journal of Software Maintenance and Evolution: Research and Practice - Special issue: Web site evolution
Mining Web Log Sequential Patterns with Position Coded Pre-Order Linked WAP-Tree
Data Mining and Knowledge Discovery
Mining interesting knowledge from weblogs: a survey
Data & Knowledge Engineering
DSM-PLW: single-pass mining of path traversal patterns over streaming web click-sequences
Computer Networks: The International Journal of Computer and Telecommunications Networking - Web dynamics
AdROSA-Adaptive personalization of web advertising
Information Sciences: an International Journal
Efficient sequential access pattern mining for web recommendations
International Journal of Knowledge-based and Intelligent Engineering Systems
Frequent Closed Sequence Mining without Candidate Maintenance
IEEE Transactions on Knowledge and Data Engineering
Incremental and interactive mining of web traversal patterns
Information Sciences: an International Journal
Web usage mining with intentional browsing data
Expert Systems with Applications: An International Journal
Efficient mining of sequential patterns with time constraints: Reducing the combinations
Expert Systems with Applications: An International Journal
An adaptive website system to improve efficiency with web mining techniques
Advanced Engineering Informatics
Efficient mining and prediction of user behavior patterns in mobile web systems
Information and Software Technology
Web usage mining for analysing elder self-care behavior patterns
Expert Systems with Applications: An International Journal
User Behaviour Pattern Mining from Weblog
International Journal of Data Warehousing and Mining
Fuzzy classification in web usage mining using fuzzy quantifiers
Proceedings of the 2013 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining
Mining cluster-based patterns for elder self-care behavior
AusDM '12 Proceedings of the Tenth Australasian Data Mining Conference - Volume 134
| Hi-index | 12.06 |
Understanding the navigational behaviour of website visitors is a significant factor of success in the emerging business models of electronic commerce and even mobile commerce. However, Web traversal patterns obtained by traditional Web usage mining approaches are ineffective for the content management of websites. They do not provide the big picture of the intentions of the visitors. The Web navigation patterns, termed throughout-surfing patterns (TSPs) as defined in this paper, are a superset of Web traversal patterns that effectively display the trends toward the next visited Web pages in a browsing session. TSPs are more expressive for understanding the purposes of website visitors. In this paper, we first introduce the concept of throughout-surfing patterns and then present an efficient method for mining the patterns. We propose a compact graph structure, termed a path traversal graph, to record information about the navigation paths of website visitors. The graph contains the frequent surfing paths that are required for mining TSPs. In addition, we devised a graph traverse algorithm based on the proposed graph structure to discover the TSPs. The experimental results show the proposed mining method is highly efficient to discover TSPs.