Dynamic itemset counting and implication rules for market basket data
SIGMOD '97 Proceedings of the 1997 ACM SIGMOD international conference on Management of data
MultiMediaMiner: a system prototype for multimedia data mining
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
FreeSpan: frequent pattern-projected sequential pattern mining
Proceedings of the sixth ACM SIGKDD international conference on Knowledge discovery and data mining
Permutation Generation Methods
ACM Computing Surveys (CSUR)
SPADE: an efficient algorithm for mining frequent sequences
Machine Learning
Mining Sequential Patterns: Generalizations and Performance Improvements
EDBT '96 Proceedings of the 5th International Conference on Extending Database Technology: Advances in Database Technology
Fast Algorithms for Mining Association Rules in Large Databases
VLDB '94 Proceedings of the 20th International Conference on Very Large Data Bases
CASCON '98 Proceedings of the 1998 conference of the Centre for Advanced Studies on Collaborative research
Mining Recurrent Items in Multimedia with Progressive Resolution Refinement
ICDE '00 Proceedings of the 16th International Conference on Data Engineering
Resource and knowledge discovery from the internet and multimedia repositories
Resource and knowledge discovery from the internet and multimedia repositories
Mining Sequential Patterns by Pattern-Growth: The PrefixSpan Approach
IEEE Transactions on Knowledge and Data Engineering
Video Data Mining: Semantic Indexing and Event Detection from the Association Perspective
IEEE Transactions on Knowledge and Data Engineering
Data Mining: Concepts and Techniques
Data Mining: Concepts and Techniques
Sequential association mining for video summarization
ICME '03 Proceedings of the 2003 International Conference on Multimedia and Expo - Volume 3 (ICME '03) - Volume 03
Mining video associations for efficient database management
IJCAI'03 Proceedings of the 18th international joint conference on Artificial intelligence
| Hi-index | 0.00 |
Video Association Mining(VAM) is the process of discovering associations in a given video. Two key phases of VAM are (i) Transformation and (ii) Frequent Temporal Pattern Mining. The transformation phase converts the original input video to an alternate transactional format, namely a cluster sequence. Frequent temporal pattern mining phase concerns the generation of patterns subject to the temporal distance and support thresholds. The paper addresses the issue of frequent temporal pattern mining and studies algorithms for the same. The existing Apriori based algorithm is compared with three other approaches highlighting the case specific situations suited by each.