ACM Computing Surveys (CSUR) - Annals of discrete mathematics, 24
Information retrieval: data structures and algorithms
Information retrieval: data structures and algorithms
Mining association rules between sets of items in large databases
SIGMOD '93 Proceedings of the 1993 ACM SIGMOD international conference on Management of data
Query flocks: a generalization of association-rule mining
SIGMOD '98 Proceedings of the 1998 ACM SIGMOD international conference on Management of data
Integrating association rule mining with relational database systems: alternatives and implications
SIGMOD '98 Proceedings of the 1998 ACM SIGMOD international conference on Management of data
Fast Algorithms for Mining Association Rules in Large Databases
VLDB '94 Proceedings of the 20th International Conference on Very Large Data Bases
Discovery of Multiple-Level Association Rules from Large Databases
VLDB '95 Proceedings of the 21th International Conference on Very Large Data Bases
Mining Generalized Association Rules
VLDB '95 Proceedings of the 21th International Conference on Very Large Data Bases
Preknowledge-based generalized association rules mining
Journal of Intelligent & Fuzzy Systems: Applications in Engineering and Technology
Generalized association rule mining using an efficient data structure
Expert Systems with Applications: An International Journal
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This paper proposes an efficient method for data mining of generalized association rules on the basis of partial-match retrieval. A generalized association rule is derived from regularities of data patterns, which are found in the database under a given data hierarchy with enough frequencies. The pattern search is a central part of data mining of this type and occupies most of the running time. In this paper, we regard a data pattern as a partial-match query in partial-match retrieval then the pattern search becomes a problem to find partial-match queries of which answers include sufficient number of database records. The proposed method consists of a selective enumeration of candidate queries and an efficient partial-match retrieval using signatures. A signature, which is a bit sequence of fixed length, is associated with data, a record and a query. The answer for a query is fast computed by bit operations among the signatures. The proposed data mining method is realized based on an extended signature method that can deal with a data hierarchy. We also discuss design issues and mathematical properties of the method.