PLWAP sequential mining: open source code
Proceedings of the 1st international workshop on open source data mining: frequent pattern mining implementations
Mining frequent web access patterns with partial enumeration
ACM-SE 45 Proceedings of the 45th annual southeast regional conference
Effective pruning strategies for sequential pattern mining
Proceedings of the 1st international conference on Forensic applications and techniques in telecommunications, information, and multimedia and workshop
Efficient algorithms for incremental maintenance of closed sequential patterns in large databases
Data & Knowledge Engineering
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
Web mining technique framework for intelligent e-business applications
ICCOMP'08 Proceedings of the 12th WSEAS international conference on Computers
Mining frequent sequential patterns with first-occurrence forests
Proceedings of the 46th Annual Southeast Regional Conference on XX
TidFP: Mining Frequent Patterns in Different Databases with Transaction ID
DaWaK '09 Proceedings of the 11th International Conference on Data Warehousing and Knowledge Discovery
Mining very long sequences in large databases with PLWAPLong
IDEAS '09 Proceedings of the 2009 International Database Engineering & Applications Symposium
Identifying web navigation behaviour and patterns automatically from clickstream data
International Journal of Web Engineering and Technology
Efficient web usage mining process for sequential patterns
Proceedings of the 11th International Conference on Information Integration and Web-based Applications & Services
A taxonomy of sequential pattern mining algorithms
ACM Computing Surveys (CSUR)
WebUser: mining unexpected web usage
International Journal of Business Intelligence and Data Mining
Mining Web navigation patterns with a path traversal graph
Expert Systems with Applications: An International Journal
Mining uncertain web log sequences with access history probabilities
Proceedings of the 2011 ACM Symposium on Applied Computing
Efficient incremental mining of frequent sequence generators
DASFAA'11 Proceedings of the 16th international conference on Database systems for advanced applications - Volume Part I
Mining maximum frequent access patterns in web logs based on unique labeled tree
WISE'06 Proceedings of the 7th international conference on Web Information Systems
A distributed recommender system architecture
International Journal of Web Engineering and Technology
User Behaviour Pattern Mining from Weblog
International Journal of Data Warehousing and Mining
An efficient tree-based algorithm for mining sequential patterns with multiple minimum supports
Journal of Systems and Software
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Sequential mining is the process of applying data mining techniques to a sequential database for the purposes of discovering the correlation relationships that exist among an ordered list of events. An important application of sequential mining techniques is web usage mining, for mining web log accesses, where the sequences of web page accesses made by different web users over a period of time, through a server, are recorded. Web access pattern tree (WAP-tree) mining is a sequential pattern mining technique for web log access sequences, which first stores the original web access sequence database on a prefix tree, similar to the frequent pattern tree (FP-tree) for storing non-sequential data. WAP-tree algorithm then, mines the frequent sequences from the WAP-tree by recursively re-constructing intermediate trees, starting with suffix sequences and ending with prefix sequences.This paper proposes a more efficient approach for using the WAP-tree to mine frequent sequences, which totally eliminates the need to engage in numerous re-construction of intermediate WAP-trees during mining. The proposed algorithm builds the frequent header node links of the original WAP-tree in a pre-order fashion and uses the position code of each node to identify the ancestor/descendant relationships between nodes of the tree. It then, finds each frequent sequential pattern, through progressive prefix sequence search, starting with its first prefix subsequence event. Experiments show huge performance gain over the WAP-tree technique.