ExAMiner: Optimized Level-wise Frequent Pattern Mining with Monotone Constraints
ICDM '03 Proceedings of the Third IEEE International Conference on Data Mining
Statistical properties of transactional databases
Proceedings of the 2004 ACM symposium on Applied computing
A new algorithm for gap constrained sequence mining
Proceedings of the 2004 ACM symposium on Applied computing
Distributed approximate mining of frequent patterns
Proceedings of the 2005 ACM symposium on Applied computing
Tight upper bounds on the number of candidate patterns
ACM Transactions on Database Systems (TODS)
Fast and Memory Efficient Mining of Frequent Closed Itemsets
IEEE Transactions on Knowledge and Data Engineering
KDDML: a middleware language and system for knowledge discovery in databases
Data & Knowledge Engineering
Distributed knowledge discovery with the parallel KDDML system
PDCN'06 Proceedings of the 24th IASTED international conference on Parallel and distributed computing and networks
Soft constraint based pattern mining
Data & Knowledge Engineering
Approximate mining of frequent patterns on streams
Intelligent Data Analysis - Knowlegde Discovery from Data Streams
Mining top-k frequent patterns in the presence of the memory constraint
The VLDB Journal — The International Journal on Very Large Data Bases
The VLDB Journal — The International Journal on Very Large Data Bases
A constraint-based querying system for exploratory pattern discovery
Information Systems
Finding Frequent Closed Itemsets in Sliding Window in Linear Time
IEICE - Transactions on Information and Systems
An efficient parallel and distributed algorithm for counting frequent sets
VECPAR'02 Proceedings of the 5th international conference on High performance computing for computational science
On interactive pattern mining from relational databases
KDID'06 Proceedings of the 5th international conference on Knowledge discovery in inductive databases
GC-tree: a fast online algorithm for mining frequent closed itemsets
PAKDD'07 Proceedings of the 2007 international conference on Emerging technologies in knowledge discovery and data mining
TGC-tree: an online algorithm tracing closed itemset and transaction set simultaneously
LKR'08 Proceedings of the 3rd international conference on Large-scale knowledge resources: construction and application
BISC: A bitmap itemset support counting approach for efficient frequent itemset mining
ACM Transactions on Knowledge Discovery from Data (TKDD)
Intelligent Data Analysis - Ubiquitous Knowledge Discovery
Interestingness is not a dichotomy: introducing softness in constrained pattern mining
PKDD'05 Proceedings of the 9th European conference on Principles and Practice of Knowledge Discovery in Databases
Programming relational databases for Itemset mining over large transactional tables
EPIA'05 Proceedings of the 12th Portuguese conference on Progress in Artificial Intelligence
A relational query primitive for constraint-based pattern mining
Proceedings of the 2004 European conference on Constraint-Based Mining and Inductive Databases
Proceedings of the 2004 European conference on Constraint-Based Mining and Inductive Databases
A hybrid GRASP with data mining for the maximum diversity problem
HM'05 Proceedings of the Second international conference on Hybrid Metaheuristics
International Journal of Distributed Systems and Technologies
Self-configuring data mining for ubiquitous computing
Information Sciences: an International Journal
A hybrid data mining GRASP with path-relinking
Computers and Operations Research
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The performance of an algorithm that mines frequent sets from transactional databases may severely depend on the specific features of the data being analyzed. Moreover, some architectural characteristics of the computational platform used - e.g. the available main memory - can dramatically change its runtime behavior. In this paper we present DCI (Direct Count & Intersect), an efficient algorithm for discovering frequent sets from large databases. Due to the multiple heuristics strategies adopted, DCI can adapt its behavior not only to the features of the specific computing platform, but also to the features of the datasetbeing mined, so that it results very effective in mining both short and long patterns from sparse and dense datasets. Finally we also discuss the parallelization strategies adopted in the design of ParDCI, a distributed and multi-threaded implementation of DCI.