Introduction to object-oriented databases
Introduction to object-oriented databases
Mining association rules between sets of items in large databases
SIGMOD '93 Proceedings of the 1993 ACM SIGMOD international conference on Management of data
An effective hash-based algorithm for mining association rules
SIGMOD '95 Proceedings of the 1995 ACM SIGMOD international conference on Management of data
Dynamic itemset counting and implication rules for market basket data
SIGMOD '97 Proceedings of the 1997 ACM SIGMOD international conference on Management of data
Mining association rules with multiple minimum supports
KDD '99 Proceedings of the fifth ACM SIGKDD international conference on Knowledge discovery and data mining
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
An Efficient Algorithm for Mining Association Rules in 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
Sampling Large Databases for Association Rules
VLDB '96 Proceedings of the 22th International Conference on Very Large Data Bases
Hi-index | 0.89 |
In real life applications the dominant model of the single support, which assumed all itemsets to be of the same nature and importance proved defective. The non-homogeneity of the itemsets on one hand and the non-uniformity of their number of appearances on the other require that we use different approaches. Some techniques have been proposed thus far trying to address these inefficiencies, but then new more demanding questions arose such as which itemsets are more interesting than others, what distinguishes them and how should they be identified, and finally how they should be effectively handled. Furthermore one common drawback of all approaches is that they have a tremendous lag in discovering new relationships and work only with long existing relationships or patterns. We propose a method that finds what we define as 'hot' itemsets in our database, deals with all problems described above and yet proves very efficient.