Mining association rules between sets of items in large databases
SIGMOD '93 Proceedings of the 1993 ACM SIGMOD international conference on Management of data
Mining frequent patterns without candidate generation
SIGMOD '00 Proceedings of the 2000 ACM SIGMOD international conference on Management of data
MAFIA: A Maximal Frequent Itemset Algorithm for Transactional Databases
Proceedings of the 17th International Conference on Data Engineering
Mining Strong Affinity Association Patterns in Data Sets with Skewed Support Distribution
ICDM '03 Proceedings of the Third IEEE International Conference on Data Mining
Fast vertical mining using diffsets
Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining
A Hybrid Approach for Mining Maixmal Hyperclique Patterns
ICTAI '04 Proceedings of the 16th IEEE International Conference on Tools with Artificial Intelligence
An association analysis approach to biclustering
Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining
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Hyperclique patterns are groups of objects which are strongly related to each other. Indeed, the objects in a hyperclique pattern have a guaranteed level of global pairwise similarity to one another as measured by uncentered Pearson's correlation coefficient. Recent literature has provided the approach to discovering hyperclique patterns over data sets with binary attributes. In this paper, we introduce algorithms for mining maximal hyperclique patterns in large data sets containing quantitative attributes. An intuitive and simple solution is to partition quantitative attributes into binary attributes. However, there is potential information loss due to partitioning. Instead, our approach is based on a normalization scheme and can directly work on quantitative attributes. In addition, we adopt the algorithm structures of three popular association pattern mining algorithms and add a critical clique pruning technique. Finally, we compare the performance of these algorithms for finding quantitative maximal hyperclique patterns using some real-world data sets.