Mining association rules between sets of items in large databases
SIGMOD '93 Proceedings of the 1993 ACM SIGMOD international conference on Management of data
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SIGMOD '95 Proceedings of the 1995 ACM SIGMOD international conference on Management of data
Dynamic itemset counting and implication rules for market basket data
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From data mining to knowledge discovery: an overview
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SIGMOD '98 Proceedings of the 1998 ACM SIGMOD international conference on Management of data
Mining frequent patterns without candidate generation
SIGMOD '00 Proceedings of the 2000 ACM SIGMOD international conference on Management of data
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Journal of Parallel and Distributed Computing - Special issue on high-performance data mining
Real world performance of association rule algorithms
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IEEE Transactions on Knowledge and Data Engineering
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ICML '02 Proceedings of the Nineteenth International Conference on Machine Learning
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ICDE '98 Proceedings of the Fourteenth International Conference on Data Engineering
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ICDM '01 Proceedings of the 2001 IEEE International Conference on Data Mining
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VLDB '94 Proceedings of the 20th International Conference on Very Large Data Bases
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VLDB '95 Proceedings of the 21th International Conference on Very Large Data Bases
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WEBKDD '01 Revised Papers from the Third International Workshop on Mining Web Log Data Across All Customers Touch Points
Selecting the right interestingness measure for association patterns
Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining
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Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining
Discovery of Interesting Association Rules from Livelink Web Log Data
ICDM '02 Proceedings of the 2002 IEEE International Conference on Data Mining
Mining Frequent Patterns without Candidate Generation: A Frequent-Pattern Tree Approach
Data Mining and Knowledge Discovery
Pattern-growth methods for frequent pattern mining
Pattern-growth methods for frequent pattern mining
Efficient mining of indirect associations using HI-mine
AI'03 Proceedings of the 16th Canadian society for computational studies of intelligence conference on Advances in artificial intelligence
Mining Indirect Association Rules for Web Recommendation
International Journal of Applied Mathematics and Computer Science
A generic approach for mining indirect association rules in data streams
IEA/AIE'11 Proceedings of the 24th international conference on Industrial engineering and other applications of applied intelligent systems conference on Modern approaches in applied intelligence - Volume Part I
| Hi-index | 0.00 |
Discovering association rules is one of the important tasks in data mining. While most of the existing algorithms are developed for efficient mining of frequent patterns, it has been noted recently that some of the infrequent patterns, such as indirect associations, provide useful insight into the data. In this paper, we propose an efficient algorithm, called HI-mine, based on a new data structure, called HI-struct, for mining the complete set of indirect associations between items. Our experimental results show that HI-mine's performance is significantly better than that of the previously developed algorithm for mining indirect associations on both synthetic and real world data sets over practical ranges of support specifications.