Mining association rules between sets of items in large databases
SIGMOD '93 Proceedings of the 1993 ACM SIGMOD international conference on Management of data
A new and versatile method for association generation
Information Systems
Fast algorithms for mining association rules
Readings in database systems (3rd ed.)
Mining frequent patterns without candidate generation
SIGMOD '00 Proceedings of the 2000 ACM SIGMOD international conference on Management of data
Fundamentals of Database Systems
Fundamentals of Database Systems
Scalable Algorithms for Association Mining
IEEE Transactions on Knowledge and Data Engineering
DEMON: Mining and Monitoring Evolving Data
IEEE Transactions on Knowledge and Data Engineering
Memory issues in frequent itemset mining
Proceedings of the 2004 ACM symposium on Applied computing
COFI approach for mining frequent itemsets revisited
Proceedings of the 9th ACM SIGMOD workshop on Research issues in data mining and knowledge discovery
Constructing Suffix Tree for Gigabyte Sequences with Megabyte Memory
IEEE Transactions on Knowledge and Data Engineering
CanTree: A Tree Structure for Efficient Incremental Mining of Frequent Patterns
ICDM '05 Proceedings of the Fifth IEEE International Conference on Data Mining
Constructing complete FP-Tree for incremental mining of frequent patterns in dynamic databases
IEA/AIE'06 Proceedings of the 19th international conference on Advances in Applied Artificial Intelligence: industrial, Engineering and Other Applications of Applied Intelligent Systems
A persistent HY-Tree to efficiently support itemset mining on large datasets
Proceedings of the 2010 ACM Symposium on Applied Computing
HUC-Prune: an efficient candidate pruning technique to mine high utility patterns
Applied Intelligence
Mining bridging rules between conceptual clusters
Applied Intelligence
Interactive mining of high utility patterns over data streams
Expert Systems with Applications: An International Journal
Mining interesting user behavior patterns in mobile commerce environments
Applied Intelligence
Mining high utility itemsets by dynamically pruning the tree structure
Applied Intelligence
Frequent episode mining within the latest time windows over event streams
Applied Intelligence
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Frequent itemset mining methods basically address time scalability and greatly rely on available physical memory. However, the size of real-world databases to be mined is exponentially increasing, and hence main memory size is a serious bottleneck of the existing methods. So, it is necessary to develop new methods that do not fully rely on physical memory; new methods that utilize the secondary storage in the mining process should be the target. This motivates the work described in this paper; we mainly propose (Disk Resident Frequent Pattern) DRFP-Growth as a disk based approach similar to FP-Growth. DRFP-growth uses DRFP-tree, which is treated exactly as FP-tree when constructed in main memory and gets into a modified structure when it turns into disk resident to overcome the main memory bottleneck. This way, we are able to mine for frequent itemsets from databases of arbitrary sizes without being restricted by the available physical memory. In other words, we initially try to mine the database using the original FP-growth; we expand into the secondary memory only if we run out of physical memory. So, DRFP-growth is very comparable to FP-growth for small databases and high support threshold values. On the other hand, using DRFP-growth, we are still able to mine huge databases for low support threshold values (the only limitation is the available secondary storage rather than physical memory). The reported test results demonstrate how the proposed approach succeeds for cases where main memory based approaches fail.