Software Defect Association Mining and Defect Correction Effort Prediction
IEEE Transactions on Software Engineering
Interestingness measures for data mining: A survey
ACM Computing Surveys (CSUR)
On a confidence gain measure for association rule discovery and scoring
The VLDB Journal — The International Journal on Very Large Data Bases
Evaluation campaigns and TRECVid
MIR '06 Proceedings of the 8th ACM international workshop on Multimedia information retrieval
Data Mining: Practical Machine Learning Tools and Techniques, Second Edition (Morgan Kaufmann Series in Data Management Systems)
Challenges and Interesting Research Directions in Associative Classification
ICDMW '06 Proceedings of the Sixth IEEE International Conference on Data Mining - Workshops
Correlation-Based Video Semantic Concept Detection Using Multiple Correspondence Analysis
ISM '08 Proceedings of the 2008 Tenth IEEE International Symposium on Multimedia
Association and Temporal Rule Mining for Post-Filtering of Semantic Concept Detection in Video
IEEE Transactions on Multimedia
Correlation maximisation-based discretisation for supervised classification
International Journal of Business Intelligence and Data Mining
Rule-Based Semantic Concept Classification from Large-Scale Video Collections
International Journal of Multimedia Data Engineering & Management
Content-Based Multimedia Retrieval Using Feature Correlation Clustering and Fusion
International Journal of Multimedia Data Engineering & Management
| Hi-index | 0.00 |
The technique of performing classification using association rule mining (ARM) has been adopted to bridge the multimedia semantic gap between low-level features and high-level concepts of interest, taking advantages of both classification and association rule mining. One of the most important research approaches in ARM is to investigate the interestingness measure which plays a key role in association rule discovery stage and rule selection stage. In this paper, a new correlation-based interestingness measure that is used at both stages is proposed. The association rules are generated by a novel interestingness measure obtained from applying multiple correspondence analysis (MCA) to explore the correlation between two feature-value pairs and concept classes. Then the correlation-based interestingness measure is reused and aggregated with the inter-similarity and intra-similarity values to rank the final rule set for classification. Detecting the concepts from the benchmark data provided by the TRECVID project, we have shown that our proposed framework achieves higher accuracy than the classifiers that are commonly applied to multimedia retrieval.