A localized algorithm for parallel association mining
Proceedings of the ninth annual ACM symposium on Parallel algorithms and architectures
Efficient mining of association rules in text databases
Proceedings of the eighth international conference on Information and knowledge management
Algorithms for association rule mining — a general survey and comparison
ACM SIGKDD Explorations Newsletter
Parallel Algorithms for Discovery of Association Rules
Data Mining and Knowledge Discovery
A lattice-based approach for I/O efficient association rule mining
Information Systems - Databases: Creation, management and utilization
Scalable Parallel Data Mining for Association Rules
IEEE Transactions on Knowledge and Data Engineering
Exploiting Dataset Similarity for Distributed Mining
IPDPS '00 Proceedings of the 15 IPDPS 2000 Workshops on Parallel and Distributed Processing
Clustering Distributed Homogeneous Datasets
PKDD '00 Proceedings of the 4th European Conference on Principles of Data Mining and Knowledge Discovery
Computing Association Rules Using Partial Totals
PKDD '01 Proceedings of the 5th European Conference on Principles of Data Mining and Knowledge Discovery
Efficiently Mining Approximate Models of Associations in Evolving Databases
PKDD '02 Proceedings of the 6th European Conference on Principles of Data Mining and Knowledge Discovery
Association Rules & Evolution in Time
SETN '02 Proceedings of the Second Hellenic Conference on AI: Methods and Applications of Artificial Intelligence
A Theoretical Framework for Association Mining Based on the Boolean Retrieval Model
DaWaK '01 Proceedings of the Third International Conference on Data Warehousing and Knowledge Discovery
Efficient Mining for Association Rules with Relational Database Systems
IDEAS '99 Proceedings of the 1999 International Symposium on Database Engineering & Applications
Tree Structures for Mining Association Rules
Data Mining and Knowledge Discovery
Fast vertical mining using diffsets
Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining
On computing, storing and querying frequent patterns
Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining
Carpenter: finding closed patterns in long biological datasets
Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining
A high-performance distributed algorithm for mining association rules
Knowledge and Information Systems
ACM Computing Surveys (CSUR)
Distributed Mining of Maximal Frequent Itemsets on a Data Grid System
The Journal of Supercomputing
Summarizing itemset patterns using probabilistic models
Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining
Data & Knowledge Engineering
Removing biases in unsupervised learning of sequential patterns
Intelligent Data Analysis
Efficient mining of maximal frequent itemsets from databases on a cluster of workstations
Knowledge and Information Systems
An Efficient Association Rule Mining Algorithm for Classification
ICAISC '08 Proceedings of the 9th international conference on Artificial Intelligence and Soft Computing
A Novel Incremental Algorithm for Frequent Itemsets Mining in Dynamic Datasets
CIARP '08 Proceedings of the 13th Iberoamerican congress on Pattern Recognition: Progress in Pattern Recognition, Image Analysis and Applications
Evolutionary Extraction of Association Rules: A Preliminary Study on their Effectiveness
HAIS '09 Proceedings of the 4th International Conference on Hybrid Artificial Intelligence Systems
Mining spatial object associations for scientific data
IJCAI'05 Proceedings of the 19th international joint conference on Artificial intelligence
PFunc: modern task parallelism for modern high performance computing
Proceedings of the Conference on High Performance Computing Networking, Storage and Analysis
Robust and distributed top-n frequent-pattern mining with SAP BW accelerator
Proceedings of the VLDB Endowment
Algorithms for mining frequent itemsets in static and dynamic datasets
Intelligent Data Analysis
Memory-efficient frequent-itemset mining
Proceedings of the 14th International Conference on Extending Database Technology
Executing association rule mining algorithms under a Grid computing environment
Proceedings of the Workshop on Parallel and Distributed Systems: Testing, Analysis, and Debugging
Making use of associative classifiers in order to alleviate typical drawbacks in recommender systems
Expert Systems with Applications: An International Journal
pcApriori: scalable apriori for multiprocessor systems
Proceedings of the 25th International Conference on Scientific and Statistical Database Management
Scalable frequent itemset mining on many-core processors
Proceedings of the Ninth International Workshop on Data Management on New Hardware
Mining frequent itemsets in data streams within a time horizon
Data & Knowledge Engineering
A time-efficient breadth-first level-wise lattice-traversal algorithm to discover rare itemsets
Data Mining and Knowledge Discovery
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Association rule discovery has emerged as an important problem in knowledge discovery and data mining. The association mining task consists of identifying the frequent itemsets, and then forming conditional implication rules among them. In this paper we present efficient algorithms for the discovery of frequent itemsets, which forms the compute intensive phase of the task. The algorithms utilize the structural properties of frequent itemsets to facilitate fast discovery. The related database items are grouped together into clusters representing the potential maximal frequent itemsets in the database. Each cluster induces a sub-lattice of the itemset lattice. Efficient lattice traversal techniques are presented, which quickly identify all the true maximal frequent itemsets, and all their subsets if desired. We also present the effect of using different database layout schemes combined with the proposed clustering and traversal techniques. The proposed algorithms scan a (pre-processed) database only once, addressing the open question in association mining, whether all the rules can be efficiently extracted in a single database pass. We experimentally compare the new algorithms against the previous approaches, obtaining improvements of more than an order of magnitude for our test databases.