Bottom-Up Association Rule Mining in Relational Databases
Journal of Intelligent Information Systems - Special issue on data warehousing and knowledge discovery
Programming the K-means clustering algorithm in SQL
Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining
Integrating K-Means Clustering with a Relational DBMS Using SQL
IEEE Transactions on Knowledge and Data Engineering
Logic-Based association rule mining in XML documents
APWeb'06 Proceedings of the 2006 international conference on Advanced Web and Network Technologies, and Applications
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Although there have been several encouraging attempts at developing methods for data mining using SQL, simplicity and efficiency still remain significant impediments for furtherdevelopment. In this paper, we propose a significantly new approach and show that any object relational database can be mined for association rules without any restructuring orpreprocessing using only basic SQL3 constructs and functions, and hence no additional machineries are necessary. In particular, we show that the cost of computing associationrules for a given database does not depend on support and confidence thresholds. More precisely, the set of large items can be computed using one simple join query and anaggregation once the set of all possible meets (least fix point) of item set patterns in the input table is known. The principal focus of this paper is to demonstrate that several SQL3expressions exists for the mining of association rules.