Data Mining: An Overview from a Database Perspective
IEEE Transactions on Knowledge and Data Engineering
Efficient Data Mining for Path Traversal Patterns
IEEE Transactions on Knowledge and Data Engineering
A Strategic Framework for Developing Electronic Commerce
IEEE Internet Computing
Fast Algorithms for Mining Association Rules in Large Databases
VLDB '94 Proceedings of the 20th International Conference on Very Large Data Bases
Mining Web Transaction Patterns in an Electronic Commerce Environment
PADKK '00 Proceedings of the 4th Pacific-Asia Conference on Knowledge Discovery and Data Mining, Current Issues and New Applications
Discovering Web Access Patterns and Trends by Applying OLAP and Data Mining Technology on Web Logs
ADL '98 Proceedings of the Advances in Digital Libraries Conference
A process of knowledge discovery from web log data: Systematization and critical review
Journal of Intelligent Information Systems
An efficient hierarchical clustering model for grouping web transactions
International Journal of Business Intelligence and Data Mining
A practical extension of web usage mining with intentional browsing data toward usage
Expert Systems with Applications: An International Journal
Mining Frequent Purchase Behavior Patterns for Commercial Websites
ICCCI '09 Proceedings of the 1st International Conference on Computational Collective Intelligence. Semantic Web, Social Networks and Multiagent Systems
Expert Systems with Applications: An International Journal
A new algorithm to discover page-action rules on web
KES'05 Proceedings of the 9th international conference on Knowledge-Based Intelligent Information and Engineering Systems - Volume Part III
Discovering valuable user behavior patterns in mobile commerce environments
PAKDD'11 Proceedings of the 15th international conference on New Frontiers in Applied Data Mining
Mining interesting user behavior patterns in mobile commerce environments
Applied Intelligence
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In this paper, we explore the data mining capability, which involves mining Web transaction patterns for an electronic commerce (EC) environment. To better reflect the customer usage patterns in the EC environment, we propose a mining model that takes both the traveling patterns and purchasing behavior of customers into consideration. We devise two efficient algorithms (MTSPJ and MTSPC) for determining the frequent transaction patterns, which are termed large transaction patterns in this paper. In addition, algorithm WTM devised in our prior work, is used for comparison purposes. By utilizing the path-trimming technique, which is developed, to exploit the relationship between traveling and purchasing behaviors, MTSPJ and MTSPC are able to generate the large transaction patterns very efficiently. A simulation model for the EC environment is developed and a synthetic workload is generated for performance studies.