Mining association rules between sets of items in large databases
SIGMOD '93 Proceedings of the 1993 ACM SIGMOD international conference on Management of data
Mining in a data-flow environment: experience in network intrusion detection
KDD '99 Proceedings of the fifth ACM SIGKDD international conference on Knowledge discovery and data mining
Mining frequent patterns without candidate generation
SIGMOD '00 Proceedings of the 2000 ACM SIGMOD international conference on Management of data
Scalable Algorithms for Association Mining
IEEE Transactions on Knowledge and Data Engineering
A Support-Ordered Trie for Fast Frequent Itemset Discovery
IEEE Transactions on Knowledge and Data Engineering
Tornado: A self-reconfiguration control system for core-based multiprocessor CSoPCs
Journal of Systems Architecture: the EUROMICRO Journal
CPM: A collaborative process modeling for cooperative manufacturers
Advanced Engineering Informatics
Incremental and interactive mining of web traversal patterns
Information Sciences: an International Journal
Mining association rules from imprecise ordinal data
Fuzzy Sets and Systems
Mining fuzzy association rules from questionnaire data
Knowledge-Based Systems
Sliding window-based frequent pattern mining over data streams
Information Sciences: an International Journal
Processing online analytics with classification and association rule mining
Knowledge-Based Systems
Anomaly intrusion detection by clustering transactional audit streams in a host computer
Information Sciences: an International Journal
Mining frequent patterns from network flows for monitoring network
Expert Systems with Applications: An International Journal
Self-adaptive and dynamic clustering for online anomaly detection
Expert Systems with Applications: An International Journal
Mining association rules for the quality improvement of the production process
Expert Systems with Applications: An International Journal
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Recently, manufacturing companies have been attempting to increase competitiveness in their business collaboration with cooperative companies rather than within their own companies. In order to facilitate their collaboration, they are attempting to adopt or already using a collaboration system, which supports a number of functions and services. However, it is very difficult to apply existing systems into other organizations or industrial sections without customization or reconfiguration because functional or service requirements of users usually differ according to their domain knowledge. In order to re-apply and disseminate an existing system to other companies, therefore, the system must be reconfigured by modifying, upgrading, or newly developing some portions of the system. During the customization processes, functions or services of the system must be refined in order to satisfy user requirements. For facilitating the reconfiguration of collaboration systems, in this paper, we first define user patterns, and subsequently propose a method for investigating and analyzing patterns based on data mining approach. The proposed method validates normal versus abnormal patterns that show a drastic increase in the use of a specific function or service, and automatically makes the system recognize abnormal patterns as new normal patterns when abnormal patterns continue for a long time. We conduct experiments and comparison studies using an Apriori-like approach in order to establish the effectiveness of the proposed method. We also suggest a guideline for the reconfiguration of function modules or services with a specific collaboration system.