Knowledge discovery in real databases: A report on the IJCAI-89 Workshop
AI Magazine - Reports from three of the 1990 Spring symposia and eight workshops held over the past two years
Mining association rules between sets of items in large databases
SIGMOD '93 Proceedings of the 1993 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
Scalable Algorithms for Association Mining
IEEE Transactions on Knowledge and Data Engineering
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
Fast vertical mining using diffsets
Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining
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As data mining techniques are explored extensively, incorporating discovered knowledge into business leads to superior competitive advantages. Most techniques in mining association rules nowadays are designed to solve problems based on de-normalized transaction files. Namely, normalized transaction tables should be transformed before mining methods could be applied, and some previous works have pointed that such data transformation usually consumes a lot of resources. As a result, this study proposes a new method which incorporates mining algorithms with enterprise transaction databases directly. In addition, in most well-known mining algorithms, the minimum support threshold is used in deciding whether the pattern is frequent or not, and it is crucial to define an appropriate threshold before performing mining tasks. Since setting an appropriate threshold cannot be done intuitively by domain experts or users, they usually set the threshold through trial and error. Usually, while setting different minimum support thresholds, most existing algorithms re-perform all mining procedures. Consequently, it takes a lot of computations. Our new method explores such circumstances and provides ways to flexibly adjust support thresholds without re-doing the whole mining task.