Knowledge discovery in databases: an overview
AI Magazine
An effective hash-based algorithm for mining association rules
SIGMOD '95 Proceedings of the 1995 ACM SIGMOD international conference on Management of data
An efficient approach to discovering knowledge from large databases
DIS '96 Proceedings of the fourth international conference on on Parallel and distributed information systems
Fast Algorithms for Mining Association Rules in Large Databases
VLDB '94 Proceedings of the 20th International Conference on Very Large Data Bases
Mining Frequent Patterns without Candidate Generation: A Frequent-Pattern Tree Approach
Data Mining and Knowledge Discovery
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Mining frequent patterns focus on discover the set of items which were frequently purchased together, which is an important data mining task and has broad applications. However, traditional frequent pattern mining does not consider the characteristics of the customers, such that the frequent patterns for some specific customer groups cannot be found. Multidimensional frequent pattern mining can find the frequent patterns according to the characteristics of the customer. Therefore, we can promote or recommend the products to a customer according to the characteristics of the customer. However, the characteristics of the customers may be the continuous data, but frequent pattern mining only can process categorical data. This paper proposes an efficient approach for mining multidimensional frequent pattern, which combines the clustering algorithm to automatically discretize numerical-type attributes without experts.