Beyond market baskets: generalizing association rules to correlations
SIGMOD '97 Proceedings of the 1997 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
ICDE '95 Proceedings of the Eleventh International Conference on Data Engineering
ICDE '98 Proceedings of the Fourteenth International Conference on Data Engineering
On the Discovery of Interesting Patterns in Association Rules
VLDB '98 Proceedings of the 24rd International Conference on Very Large Data Bases
Fast Algorithms for Mining Association Rules in Large Databases
VLDB '94 Proceedings of the 20th International Conference on Very Large Data Bases
Discovering Temporal Relation Rules Mining from Interval Data
EurAsia-ICT '02 Proceedings of the First EurAsian Conference on Information and Communication Technology
Discovering Calendar-Based Temporal Association Rules
TIME '01 Proceedings of the Eighth International Symposium on Temporal Representation and Reasoning (TIME'01)
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Mining temporal frequent patterns in transaction databases, time-series databases, and many other kinds of databases have been widely studied in data mining research. Most of the previous studies adopt an Apriori-like candidate set generation-and-test approach. However, candidate set generation is still costly, especially when there exist prolific patterns and long patterns. In this paper, we propose an efficient temporal frequent pattern mining method using the TFP-tree (Temporal Frequent Pattern tree). This approach has three advantages: (i) one can scan the transaction only once for reducing significantly the I/O cost; (ii) one can store all transactions in leaf nodes but only save the star calendar patterns in the internal nodes. So we can save a large amount of memory. Moreover, we divide the transactions into many partitions by maximum size domain which significantly saves the memory; (iii) we efficiently discover each star calendar pattern in internal node using the frequent calendar patterns of leaf node. Thus we can reduce significantly the computational time. Our performance study shows that the TFP-tree is efficient and scalable for mining, and is about an order of magnitude faster than the classical frequent pattern mining algorithms.