Mining e-commerce data: the good, the bad, and the ugly
Proceedings of the seventh ACM SIGKDD international conference on Knowledge discovery and data mining
Visualization and Analysis of Clickstream Data of Online Stores for Understanding Web Merchandising
Data Mining and Knowledge Discovery
Discovery of Web Robot Sessions Based on their Navigational Patterns
Data Mining and Knowledge Discovery
Web mining for web personalization
ACM Transactions on Internet Technology (TOIT)
Mining Client-Side Activity for Personalization
WECWIS '02 Proceedings of the Fourth IEEE International Workshop on Advanced Issues of E-Commerce and Web-Based Information Systems (WECWIS'02)
Web Usage Mining as a Tool for Personalization: A Survey
User Modeling and User-Adapted Interaction
A Framework for the Evaluation of Session Reconstruction Heuristics in Web-Usage Analysis
INFORMS Journal on Computing
APD-A Tool for Identifying Behavioural Patterns Automatically from Clickstream Data
KES '07 Knowledge-Based Intelligent Information and Engineering Systems and the XVII Italian Workshop on Neural Networks on Proceedings of the 11th International Conference
Integrating web mining and neural network for personalized e-commerce automatic service
Expert Systems with Applications: An International Journal
Identifying web navigation behaviour and patterns automatically from clickstream data
International Journal of Web Engineering and Technology
Finding unexpected navigation behaviour in clickstream data for website design improvement
Journal of Web Engineering
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When analyzing patterns in server side data, it becomes quickly apparent that some of the data originating from the client is lost, mainly due to the caching of web pages. Missing data is a very important issue when using server side data to analyze a user's browsing behavior, since the quality of the browsing patterns that can be identified depends on the quality of the data. In this paper, we present a series of experiments to demonstrate the extent of the data loss in different browsing environments and illustrate the difference this makes in the resulting browsing patterns when visualized as footstep graphs. We propose an algorithm, called the Pattern Restore Method (PRM), for restoring some of the data that has been lost and evaluate the efficiency and accuracy of this algorithm.