Discovering Patterns from Large and Dynamic Sequential Data
Journal of Intelligent Information Systems
Incremental and interactive sequence mining
Proceedings of the eighth international conference on Information and knowledge management
Mining patterns from graph traversals
Data & Knowledge Engineering
ICDE '95 Proceedings of the Eleventh International Conference on Data Engineering
PrefixSpan: Mining Sequential Patterns by Prefix-Projected Growth
Proceedings of the 17th International Conference on Data Engineering
Mining Access Patterns Efficiently from Web Logs
PADKK '00 Proceedings of the 4th Pacific-Asia Conference on Knowledge Discovery and Data Mining, Current Issues and New Applications
Analysis of navigation behaviour in web sites integrating multiple information systems
The VLDB Journal — The International Journal on Very Large Data Bases
Incremental mining of sequential patterns in large databases
Data & Knowledge Engineering
IncSpan: incremental mining of sequential patterns in large database
Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining
Incremental Mining of Web Sequential Patterns Using PLWAP Tree on Tolerance MinSupport
IDEAS '04 Proceedings of the International Database Engineering and Applications Symposium
FS-Miner: efficient and incremental mining of frequent sequence patterns in web logs
Proceedings of the 6th annual ACM international workshop on Web information and data management
Mining Web Log Sequential Patterns with Position Coded Pre-Order Linked WAP-Tree
Data Mining and Knowledge Discovery
Efficient Algorithms for Mining and Incremental Update of Maximal Frequent Sequences
Data Mining and Knowledge Discovery
PLWAP sequential mining: open source code
Proceedings of the 1st international workshop on open source data mining: frequent pattern mining implementations
Redundant association rules reduction techniques
International Journal of Business Intelligence and Data Mining
An incremental data mining algorithm for discovering web access patterns
International Journal of Business Intelligence and Data Mining
ODAM: An Optimized Distributed Association Rule Mining Algorithm
IEEE Distributed Systems Online
High Performance Parallel Database Processing and Grid Databases
High Performance Parallel Database Processing and Grid Databases
A sliding window method for finding top-k path traversal patterns over streaming Web click-sequences
Expert Systems with Applications: An International Journal
Position coded pre-order linked WAP-tree for web log sequential pattern mining
PAKDD'03 Proceedings of the 7th Pacific-Asia conference on Advances in knowledge discovery and data mining
Mining Web navigation patterns with a path traversal graph
Expert Systems with Applications: An International Journal
Mining Frequent Generalized Patterns for Web Personalization in the Presence of Taxonomies
International Journal of Data Warehousing and Mining
A New Similarity Metric for Sequential Data
International Journal of Data Warehousing and Mining
Automatic Item Weight Generation for Pattern Mining and its Application
International Journal of Data Warehousing and Mining
Weak Ratio Rules: A Generalized Boolean Association Rules
International Journal of Data Warehousing and Mining
Graph-Based Modelling of Concurrent Sequential Patterns
International Journal of Data Warehousing and Mining
Towards Comparative Mining of Web Document Objects with NFA: WebOMiner System
International Journal of Data Warehousing and Mining
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In this paper, the authors build a tree using both frequent as well as non-frequent items and named as Revised PLWAP with Non-frequent Items RePLNI-tree in single scan. While mining sequential patterns, the links related to the non-frequent items are virtually discarded. Hence, it is not required to delete or maintain the information of nodes while revising the tree for mining updated weblog. It is not required to reconstruct the tree from scratch and re-compute the patterns each time, while weblog is updated or minimum support changed, since the algorithm supports both incremental and interactive mining. The performance of the proposed tree is better, even the size of incremental database is more than 50% of existing one, while it is not so in recently proposed algorithm. For evaluation purpose, the authors have used the benchmark weblog and found that the performance of proposed tree is encouraging compared to some of the recently proposed approaches.