Mining frequent patterns without candidate generation
SIGMOD '00 Proceedings of the 2000 ACM SIGMOD international conference on Management of data
Discovering the set of fundamental rule changes
Proceedings of the seventh ACM SIGKDD international conference on Knowledge discovery and 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 Changes for Real-Life Applications
DaWaK 2000 Proceedings of the Second International Conference on Data Warehousing and Knowledge Discovery
Incremental Meta-Mining from Large Temporal Data Sets
ER '98 Proceedings of the Workshops on Data Warehousing and Data Mining: Advances in Database Technologies
CT-ITL: efficient frequent item set mining using a compressed prefix tree with pattern growth
ADC '03 Proceedings of the 14th Australasian database conference - Volume 17
Discovering association rules change from large databases
AICI'11 Proceedings of the Third international conference on Artificial intelligence and computational intelligence - Volume Part I
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An integrated approach of mining association rules and meta-rules based on a hyper-structure is put forward. In this approach, time serial databases are partitioned according to time segments, and the total number of scanning database is only twice. In the first time, a set of 1-frequent itemsets and its projection database are formed at every partition. Then every projected database is scanned to construct a hyper-structure. Through mining the hyper-structure, various rules, for example, global association rules, meta-rules, stable association rules and trend rules etc. can be obtained. Compared with existing algorithms for mining association rule, our approach can mine and obtain more useful rules. Compared with existing algorithms for meta-mining or change mining, our approach has higher efficiency. The experimental results show that our approach is very promising.