Classification using association rules has added a new dimension to the ongoing research for accurate classifiers. Over the years, a number of associative classifiers based on pos...
During the last decade, several clustering and association rule mining techniques have been applied to highlight groups of coregulated genes in gene expression data. Nowadays, inte...
When applying association mining to real datasets, a major obstacle is that often a huge number of rules are generated even with very reasonable support and confidence. Among thes...
Ping Chen, Rakesh M. Verma, Janet C. Meininger, We...
Previous studies on mining association rules find rules at single concept level, however, mining association rules at multiple concept levels may lead to the discovery of more spe...
Our work tackles the problem of finding partial determinations in databases and proposes a compressionbased measure to evaluate them. Partial determinations can be viewed as gener...
Frequent association rules (e.g., AB C to say that when properties A and B are true in a record then, C tends to be also true) have become a popular way to summarize huge datasets...
Abstract. This paper concerns the iterative implementation of a knowledge model in a data mining context. Our approach relies on coupling a Bayesian network design with an associat...
Mining association rule in event sequences is an important data mining problem with many applications. Most of previous studies on association rules are on mining intra-transaction...
Abstract. Association rule mining is a data mining technique that reveals interesting relationships in a database. Existing approaches employ different parameters to search for int...
Abstract. Data Mining is most commonly used in attempts to induce association rules from transaction data. Most previous studies focused on binaryvalued transactions, however the d...