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
Parallel Mining of Association Rules
IEEE Transactions on Knowledge and Data Engineering
What Makes Patterns Interesting in Knowledge Discovery Systems
IEEE Transactions on Knowledge and Data Engineering
Finding Interesting Patterns Using User Expectations
IEEE Transactions on Knowledge and Data Engineering
Scalable Algorithms for Association Mining
IEEE Transactions on Knowledge and Data Engineering
A New Approach to Online Generation of Association Rules
IEEE Transactions on Knowledge and Data Engineering
Extracting Share Frequent Itemsets with Infrequent Subsets
Data Mining and Knowledge Discovery
MAFIA: A Maximal Frequent Itemset Algorithm for Transactional Databases
Proceedings of the 17th 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
An Efficient Algorithm for Mining Association Rules in Large Databases
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
Mining Frequent Patterns without Candidate Generation: A Frequent-Pattern Tree Approach
Data Mining and Knowledge Discovery
Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining
Tight upper bounds on the number of candidate patterns
ACM Transactions on Database Systems (TODS)
Interestingness measures for data mining: A survey
ACM Computing Surveys (CSUR)
Mining itemset utilities from transaction databases
Data & Knowledge Engineering - Special issue: ER 2003
CanTree: a canonical-order tree for incremental frequent-pattern mining
Knowledge and Information Systems
Frequent pattern mining: current status and future directions
Data Mining and Knowledge Discovery
Incrementally fast updated frequent pattern trees
Expert Systems with Applications: An International Journal
Approximate mining of maximal frequent itemsets in data streams with different window models
Expert Systems with Applications: An International Journal
Interactive mining of top-K frequent closed itemsets from data streams
Expert Systems with Applications: An International Journal
| Hi-index | 12.05 |
Over the past decade, an increasing number of efficient algorithms have been proposed to mine frequent patterns by satisfying the minimum support threshold. Generally, determining an appropriate value for minimum support threshold is extremely difficult. This is because the appropriate value depends on the type of application and expectation of the user. Moreover, in some real-time applications such as web mining and e-business, finding new correlations between patterns by changing the minimum support threshold is needed. Since rerunning mining algorithms from scratch is very costly and time-consuming, researchers have introduced interactive mining of frequent patterns. Recently, a few efficient interactive mining algorithms have been proposed, which are able to capture the content of transaction database to eliminate possibility of the database rescanning. In this paper, we propose a new method based on prime number and its characteristics mainly for interactive mining of frequent patterns. Our method isolates the mining model from the mining process such that once the mining model is constructed; it can be frequently used by mining process with various minimum support thresholds. During the mining process, the mining algorithm reduces the number of candidate patterns and comparisons by using a new candidate set called candidate head set and several efficient pruning techniques. The experimental results verify the efficiency of our method for interactive mining of frequent patterns.