Exploratory mining and pruning optimizations of constrained associations rules
SIGMOD '98 Proceedings of the 1998 ACM SIGMOD international conference on Management of data
Integrating association rule mining with relational database systems: alternatives and implications
SIGMOD '98 Proceedings of the 1998 ACM SIGMOD international conference on Management of data
Incremental and interactive sequence mining
Proceedings of the eighth international conference on Information and knowledge management
Mining frequent patterns without candidate generation
SIGMOD '00 Proceedings of the 2000 ACM SIGMOD international conference on Management of data
Mining web logs for prediction models in WWW caching and prefetching
Proceedings of the seventh ACM SIGKDD international conference on Knowledge discovery and data mining
Efficient mining of traversal patterns
Data & Knowledge Engineering - Building web warehouse
Data Mining: Introductory and Advanced Topics
Data Mining: Introductory and Advanced Topics
Mining Sequential Patterns: Generalizations and Performance Improvements
EDBT '96 Proceedings of the 5th International Conference on Extending Database Technology: Advances in Database Technology
Fast Algorithms for Mining Association Rules in Large Databases
VLDB '94 Proceedings of the 20th International Conference on Very Large Data Bases
Web Mining: Information and Pattern Discovery on the World Wide Web
ICTAI '97 Proceedings of the 9th International Conference on Tools with Artificial Intelligence
WhatNext: A Prediction System for Web Requests using N-gram Sequence Models
WISE '00 Proceedings of the First International Conference on Web Information Systems Engineering (WISE'00)-Volume 1 - Volume 1
Evaluation of web usage mining approaches for user's next request prediction
WIDM '03 Proceedings of the 5th ACM international workshop on Web information and data management
Evaluating the markov assumption for web usage mining
WIDM '03 Proceedings of the 5th ACM international workshop on Web information and data management
Using association rules for fraud detection in web advertising networks
VLDB '05 Proceedings of the 31st international conference on Very large data bases
Web path recommendations based on page ranking and Markov models
Proceedings of the 7th annual ACM international workshop on Web information and data management
Usage-Based PageRank for Web Personalization
ICDM '05 Proceedings of the Fifth IEEE International Conference on Data Mining
Frequent pattern discovery in online environment
AIA'06 Proceedings of the 24th IASTED international conference on Artificial intelligence and applications
Web site personalization based on link analysis and navigational patterns
ACM Transactions on Internet Technology (TOIT)
Fast accumulation lattice algorithm for mining sequential patterns
ACOS'07 Proceedings of the 6th Conference on WSEAS International Conference on Applied Computer Science - Volume 6
Discovering information diffusion paths from blogosphere for online advertising
Proceedings of the 1st international workshop on Data mining and audience intelligence for advertising
Fast mining maximal sequential patterns
SMO'07 Proceedings of the 7th WSEAS International Conference on Simulation, Modelling and Optimization
Finding relevant patterns in bursty sequences
Proceedings of the VLDB Endowment
Fast mining of closed sequential patterns
WSEAS Transactions on Computers
Efficient algorithms for incremental maintenance of closed sequential patterns in large databases
Data & Knowledge Engineering
SRDFA: A Kind of Session Reconstruction DFA
NPC '08 Proceedings of the IFIP International Conference on Network and Parallel Computing
A change detection method for sequential patterns
Decision Support Systems
Identifying web navigation behaviour and patterns automatically from clickstream data
International Journal of Web Engineering and Technology
Recsplorer: recommendation algorithms based on precedence mining
Proceedings of the 2010 ACM SIGMOD International Conference on Management of data
A taxonomy of sequential pattern mining algorithms
ACM Computing Surveys (CSUR)
Analysis on repeat-buying patterns
Knowledge-Based Systems
Association rule based data mining agents for personalized web caching
COMPSAC-W'05 Proceedings of the 29th annual international conference on Computer software and applications conference
Beyond the usual suspects: context-aware revisitation support
Proceedings of the 22nd ACM conference on Hypertext and hypermedia
Mining the change of customer behavior in fuzzy time-interval sequential patterns
Applied Soft Computing
Client- and server-side revisitation prediction with SUPRA
Proceedings of the 2nd International Conference on Web Intelligence, Mining and Semantics
Sequential pattern mining -- approaches and algorithms
ACM Computing Surveys (CSUR)
User Behaviour Pattern Mining from Weblog
International Journal of Data Warehousing and Mining
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Mining frequent patterns is an important component of many prediction systems. One common usage in web applications is the mining of users' access behavior for the purpose of predicting and hence pre-fetching the web pages that the user is likely to visit. In this paper we introduce an efficient strategy for discovering frequent patterns in sequence databases that requires only two scans of the database. The first scan obtains support counts for subsequences of length two. The second scan extracts potentially frequent sequences of any length and represents them as a compressed frequent sequences tree structure (FS-tree). Frequent sequence patterns are then mined from the FS-tree. Incremental and interactive mining functionalities are also facilitated by the FS-tree. As part of this work, we developed the FS-Miner, a system that discovers frequent sequences from web log files. The FS-Miner has the ability to adapt to changes in users' behavior over time, in the form of new input sequences, and to respond incrementally without the need to perform full re-computation. Our system also allows the user to change the input parameters (e.g., minimum support and desired pattern size) interactively without requiring full re-computation in most cases. We have tested our system comparing it against two other algorithms from the literature. Our experimental results show that our system scales up linearly with the size of the input database. Furthermore, it exhibits excellent adaptability to support threshold decreases. We also show that the incremental update capability of the system provides significant performance advantages over full re-computation even for relatively large update sizes.