Time Granularities in Databases, Data Mining and Temporal Reasoning
Time Granularities in Databases, Data Mining and Temporal Reasoning
Discovering calendar-based temporal association rules
Data & Knowledge Engineering - Special issue: Temporal representation and reasoning
ICDE '98 Proceedings of the Fourteenth 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
Adding Temporal Semantics to Association Rules
PKDD '99 Proceedings of the Third European Conference on Principles of Data Mining and Knowledge Discovery
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
Mining temporal interval relational rules from temporal data
Journal of Systems and Software
An Apriori Based Approach to Improve On-line Advertising Performance
Proceedings of the 2008 conference on Applications of Data Mining in E-Business and Finance
Mining Temporal Patterns for Humanoid Robot Using Pattern Growth Method
RSFDGrC '09 Proceedings of the 12th International Conference on Rough Sets, Fuzzy Sets, Data Mining and Granular Computing
Mining dynamic association rules with comments
Knowledge and Information Systems
Efficient temporal pattern mining for humanoid robot
Proceedings of the 1st Amrita ACM-W Celebration on Women in Computing in India
HUC-Prune: an efficient candidate pruning technique to mine high utility patterns
Applied Intelligence
Single-pass incremental and interactive mining for weighted frequent patterns
Expert Systems with Applications: An International Journal
ShrFP-tree: an efficient tree structure for mining share-frequent patterns
AusDM '08 Proceedings of the 7th Australasian Data Mining Conference - Volume 87
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Associationship is an important component of data mining. In real world data the knowledge used for mining rule is almost time varying. The item have the dynamic characteristic in terms of transaction, which have seasonal selling rate and it hold time-based associationship with another item. It is also important that in database, some items which are infrequent in whole dataset but those may be frequent in a particular time period. If these items are ignored then associationship WVW200R3100221-398 result will no longer be accurate. To restrict the time based associationship calendar based pattern can be used [YPXS03]. A calendar unit such as months and days, clock units, such as hours and seconds & specialized units, such as business days and academic years, play a major role in a wide range of information system applications[BX00].Most of the popular associationship rule mining methods are having performance bottleneck for database with different characteristics. Some of the methods are efficient for sparse dataset where as some are good for a dense dataset. Our focus is to find effective time sensitive algorithm using H-struct called temporal H-mine, which takes the advantage of this data structure and dynamically adjusts links in the mining process [PHNTY01]. It is faster in traversing & advantage of precisely predictable spaces overhead. It can be scaled up to large database by database partitioning, end when dataset becomes dense, conditionally temporal FP-tree. can be constructed dynamically as part of mining.