Mining association rules between sets of items in large databases
SIGMOD '93 Proceedings of the 1993 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
Time Granularities in Databases, Data Mining and Temporal Reasoning
Time Granularities in Databases, Data Mining and Temporal Reasoning
A Survey of Temporal Knowledge Discovery Paradigms and Methods
IEEE Transactions on Knowledge and Data Engineering
ICDE '95 Proceedings of the Eleventh International Conference on Data Engineering
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
On the Discovery of Interesting Patterns in Association Rules
VLDB '98 Proceedings of the 24rd International Conference on Very Large Data Bases
Constraint-Based Rule Mining in Large, Dense Databases
ICDE '99 Proceedings of the 15th International Conference on Data Engineering
Discovering Calendar-Based Temporal Association Rules
TIME '01 Proceedings of the Eighth International Symposium on Temporal Representation and Reasoning (TIME'01)
Efficient calendar based temporal association rule
ACM SIGMOD Record
A Transaction Mapping Algorithm for Frequent Itemsets Mining
IEEE Transactions on Knowledge and Data Engineering
Keeping things simple: finding frequent item sets by recursive elimination
Proceedings of the 1st international workshop on open source data mining: frequent pattern mining implementations
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
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
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Pattern mining in temporal databases is one of the challenging platform which holds attention when some ordered sequences are frequently occurred at different time instances in the dataset. We have found temporal patterns in humanoid robot dataset of HOAP-2 (Humanoid Open-Architecture Platform) which generates different motions through recurring sequences of various joint associations. For mining temporal patterns in that dataset we have proposed a method. This method uses FP-Temporal and SH(Soft-Hyperlinked)-Temporal mining algorithm as pattern growth methods for generating temporal association rules for various motion patterns of HOAP-2. Brief performance analysis shows that SH-Temporal is much efficient than FP-Temporal for such datasets and works significantly for mining sequentially associative temporal patterns in terms of temporal association rules.