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
Efficient Mining of Intertransaction Association Rules
IEEE Transactions on Knowledge and Data Engineering
Mining for Strong Negative Associations in a Large Database of Customer Transactions
ICDE '98 Proceedings of the Fourteenth International Conference on Data Engineering
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
DaWaK 2000 Proceedings of the 4th International Conference on Data Warehousing and Knowledge Discovery
Discovery of Spatial Association Rules in Geographic Information Databases
SSD '95 Proceedings of the 4th International Symposium on Advances in Spatial Databases
Discovering Temporal Association Rules: Algorithms, Language and System
ICDE '00 Proceedings of the 16th International Conference on Data Engineering
Efficient mining of both positive and negative association rules
ACM Transactions on Information Systems (TOIS)
Accurate Classification of SAGE Data Based on Frequent Patterns of Gene Expression
ICTAI '07 Proceedings of the 19th IEEE International Conference on Tools with Artificial Intelligence - Volume 01
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Association rules mining is a popular task that involves the discovery of co-occurences of items in transaction databases Several extensions of the traditional association rules mining model have been proposed so far, however, the problem of mining for mutually exclusive items has not been investigated Such information could be useful in various cases in many application domains like bioinformatics (e.g when the expression of a gene excludes the expression of another) In this paper, we address the problem of mining pairs and triples of genes, such that the presence of one excludes the presence of the other First, we provide a concise review of the literature, then we define this problem, we propose a probability-based evaluation metric, and finally a mining algorithm that we apply on gene expression data gaining new biological insights.