Cost-based query optimization for complex pattern mining on multiple databases
EDBT '08 Proceedings of the 11th international conference on Extending database technology: Advances in database technology
Effective and efficient itemset pattern summarization: regression-based approaches
Proceedings of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining
Query Planning for Searching Inter-dependent Deep-Web Databases
SSDBM '08 Proceedings of the 20th international conference on Scientific and Statistical Database Management
Journal of Intelligent Information Systems
A rough set approach to mining connections from information systems
Proceedings of the 2010 ACM Symposium on Applied Computing
A rough set approach to multiple dataset analysis
Applied Soft Computing
CLAP: Collaborative pattern mining for distributed information systems
Decision Support Systems
EFP-M2: efficient model for mining frequent patterns in transactional database
ICCCI'12 Proceedings of the 4th international conference on Computational Collective Intelligence: technologies and applications - Volume Part II
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Many real world applications involve not just a single dataset, but a view of multiple datasets. These datasets may be collected from different sources and/or at different time instances. In such scenarios, comparing patterns or features from different datasets and understanding their relationships can be an extremely important part of the KDD process. This paper considers the problem of optimizing a mining task over multiple datasets, when it has been expressed using a highlevel interface. Specifically, we make the following contributions: 1) We present an SQL-based mechanism for querying frequent patterns across multiple datasets, and establish an algebra for these queries. 2) We develop a systematic method for enumerating query plans and present several algorithms for finding optimized query plan which reduce execution costs. 3) We evaluate our algorithms on real and synthetic datasets, and show up to an order of magnitude performance improvement