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
Issues in data stream management
ACM SIGMOD Record
Web usage mining: discovery and applications of usage patterns from Web data
ACM SIGKDD Explorations Newsletter
Web Mining: Information and Pattern Discovery on the World Wide Web
ICTAI '97 Proceedings of the 9th International Conference on Tools with Artificial Intelligence
TSP: Mining Top-K Closed Sequential Patterns
ICDM '03 Proceedings of the Third IEEE International Conference on Data Mining
ICDM '03 Proceedings of the Third IEEE International Conference on Data Mining
Mining Frequent Itemsets without Support Threshold: With and without Item Constraints
IEEE Transactions on Knowledge and Data Engineering
TFP: An Efficient Algorithm for Mining Top-K Frequent Closed Itemsets
IEEE Transactions on Knowledge and Data Engineering
DSM-TKP: Mining Top-K Path Traversal Patterns over Web Click-Streams
WI '05 Proceedings of the 2005 IEEE/WIC/ACM International Conference on Web Intelligence
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
Efficient computation of frequent and top-k elements in data streams
ICDT'05 Proceedings of the 10th international conference on Database Theory
User Behaviour Pattern Mining from Weblog
International Journal of Data Warehousing and Mining
Sliding window based weighted maximal frequent pattern mining over data streams
Expert Systems with Applications: An International Journal
Mining maximal frequent patterns by considering weight conditions over data streams
Knowledge-Based Systems
Mining top-k frequent patterns over data streams sliding window
Journal of Intelligent Information Systems
| Hi-index | 12.05 |
Online mining of path traversal patterns from Web click-streams is one of the most important problems of Web usage mining. In this paper, we propose a sliding window-based Web data mining algorithm, called Top-SW (Top-kpath traversal patterns of Stream sliding Window), to discover the set of top-k path traversal patterns from streaming maximal forward references, where k is the desired number of path traversal patterns to be mined. A new summary data structure, called Top-list (a list of Top-kpath traversal patterns) is developed to maintain the essential information about the top-k path traversal patterns from the current maximal forward references stream. Experimental studies show that the proposed Top-SW algorithm is an efficient, single-pass algorithm for mining the set of top-k path traversal patterns from a continuous stream of maximal forward references.