Models and issues in data stream systems
Proceedings of the twenty-first ACM SIGMOD-SIGACT-SIGART symposium on Principles of database systems
Efficient Data Mining for Path Traversal Patterns
IEEE Transactions on Knowledge and Data Engineering
A simple algorithm for finding frequent elements in streams and bags
ACM Transactions on Database Systems (TODS)
Mining Frequent Patterns without Candidate Generation: A Frequent-Pattern Tree Approach
Data Mining and Knowledge Discovery
Efficient evaluation of parameterized pattern queries
Proceedings of the 14th ACM international conference on Information and knowledge management
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
Data & Knowledge Engineering
Discovering geographical-specific interests from web click data
Proceedings of the first international workshop on Location and the web
Expert Systems with Applications: An International Journal
Hi-index | 0.00 |
Mining user access patterns from a continuous stream of Web-clicks presents new challenges over traditional Web usage mining in a large static Web-click database. Modeling user access patterns as maximal forward references, we present a single-pass algorithm StreamPath for online discovering frequent path traversal patterns from an extended prefix tree-based data structure which stores the compressed and essential information about user's moving histories in the stream. Theoretical analysis and performance evaluation show that the space requirement of StreamPath is limited to a logarithmic boundary, and the execution time, compared with previous multiple-pass algorithms [2], is fast.