Exploratory mining and pruning optimizations of constrained associations rules
SIGMOD '98 Proceedings of the 1998 ACM SIGMOD international conference on Management of data
Efficiently mining long patterns from databases
SIGMOD '98 Proceedings of the 1998 ACM SIGMOD international conference on Management of data
Incremental and interactive sequence mining
Proceedings of the eighth international conference on Information and knowledge management
Mining frequent patterns without candidate generation
SIGMOD '00 Proceedings of the 2000 ACM SIGMOD international conference on Management of data
Turbo-charging vertical mining of large databases
SIGMOD '00 Proceedings of the 2000 ACM SIGMOD international conference on Management of data
Data mining: concepts and techniques
Data mining: concepts and techniques
A tree projection algorithm for generation of frequent item sets
Journal of Parallel and Distributed Computing - Special issue on high-performance data mining
Maintenance of Discovered Association Rules in Large Databases: An Incremental Updating Technique
ICDE '96 Proceedings of the Twelfth International Conference on Data Engineering
Mining Frequent Item Sets with Convertible Constraints
Proceedings of the 17th International Conference on Data Engineering
H-Mine: Hyper-Structure Mining of Frequent Patterns in Large Databases
ICDM '01 Proceedings of the 2001 IEEE International Conference on Data Mining
Fast Algorithms for Mining Association Rules in Large Databases
VLDB '94 Proceedings of the 20th International Conference on Very Large Data Bases
Mining frequent item sets by opportunistic projection
Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining
Speed-up Iterative Frequent Itemset Mining with Constraint Changes
ICDM '02 Proceedings of the 2002 IEEE International Conference on Data Mining
On domination game analysis for microeconomic data mining
ACM Transactions on Knowledge Discovery from Data (TKDD)
| Hi-index | 0.00 |
In constrained data mining, users can specify constraintsto prune the search space to avoid mining uninterestingknowledge.This is typically done by specifyingsome initial values of the constraints that aresubsequently refined iteratively until satisfactory resultsare obtained.Existing mining schemes treat each iterationas a distinct mining process, and fail to exploit theinformation generated between iterations.In this paper,we propose to salvage knowledge that is discoveredfrom an earlier iteration of mining to enhance subsequentrounds of mining.In particular, we look at howfrequent patterns can be recycled.Our proposed strategyoperates in two phases.In the first phase, frequentpatterns obtained from an early iteration are used tocompress a database.In the second phase, subsequentmining processes operate on the compressed database.We propose two compression strategies and adapt threeexisting frequent pattern mining techniques to exploitthe compressed database.Results from our extensiveexperimental study show that our proposed recycling algorithmsoutperform their non-recycling counterpart byan order of magnitude.