An Architecture for Distributed Enterprise Data Mining
HPCN Europe '99 Proceedings of the 7th International Conference on High-Performance Computing and Networking
Multi-relational data mining: an introduction
ACM SIGKDD Explorations Newsletter
Prospects and challenges for multi-relational data mining
ACM SIGKDD Explorations Newsletter
Mining relational databases with multi-view learning
MRDM '05 Proceedings of the 4th international workshop on Multi-relational mining
Robust and distributed top-n frequent-pattern mining with SAP BW accelerator
Proceedings of the VLDB Endowment
Combined association rule mining
PAKDD'08 Proceedings of the 12th Pacific-Asia conference on Advances in knowledge discovery and data mining
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Many organisations have their digital information stored in a distributed systems structure scheme, be it in different locations, using vertically and horizontally distributed repositories, which brings about an high level of complexity to data mining. From a classical data mining view, where the algorithms expect a denormalised structure to be able to operate on, heterogeneous data sources, such as static demographic and dynamic transactional data are to be manipulated and integrated to the extent commercial association rules algorithms can be applied. Bearing in mind the usefulness and understandability of the application from a business perspective, combined rules of multiple patterns derived from different repositories, containing historical and point in time data, were used to produce new techniques in association mining applied to debt recovery. Initially debt repayment patterns were discovered using transactional data and class labels defined by domain expertise, then demographic patterns were attached to each of the class labels. After combining the patterns, two type of rules were discovered leading to different results: 1) same demographic pattern with different repayment patterns, and 2) same repayment pattern with different demographic patterns. The rules produced are interesting, valuable, complete and understandable, which shows the applicability and effectiveness of the new method.