Mining association rules between sets of items in large databases
SIGMOD '93 Proceedings of the 1993 ACM SIGMOD international conference on Management of data
SIGIR '02 Proceedings of the 25th annual international ACM SIGIR conference on Research and development in information retrieval
WISE '00 Proceedings of the First International Conference on Web Information Systems Engineering (WISE'00)-Volume 1 - Volume 1
Using Association Rules to Discover Search Engines Related Queries
LA-WEB '03 Proceedings of the First Conference on Latin American Web Congress
Query association surrogates for Web search: Research Articles
Journal of the American Society for Information Science and Technology
Comparing association rules and decision trees for disease prediction
HIKM '06 Proceedings of the international workshop on Healthcare information and knowledge management
Data Mining: Practical Machine Learning Tools and Techniques, Second Edition (Morgan Kaufmann Series in Data Management Systems)
A new approach to classification based on association rule mining
Decision Support Systems
Understanding the relationship of information need specificity to search query length
SIGIR '07 Proceedings of the 30th annual international ACM SIGIR conference on Research and development in information retrieval
The Combination and Evaluation of Query Performance Prediction Methods
ECIR '09 Proceedings of the 31th European Conference on IR Research on Advances in Information Retrieval
Extended gloss overlaps as a measure of semantic relatedness
IJCAI'03 Proceedings of the 18th international joint conference on Artificial intelligence
Towards a Better Ranking for Biomedical Information Retrieval Using Context
BIBM '09 Proceedings of the 2009 IEEE International Conference on Bioinformatics and Biomedicine
Estimating the query difficulty for information retrieval
Proceedings of the 33rd international ACM SIGIR conference on Research and development in information retrieval
Overview of the CLEF 2009 medical image retrieval track
CLEF'09 Proceedings of the 10th international conference on Cross-language evaluation forum: multimedia experiments
An integrated approach for medical image retrieval through combining textual and visual features
CLEF'09 Proceedings of the 10th international conference on Cross-language evaluation forum: multimedia experiments
Enhancing content-based image retrieval using machine learning techniques
AMT'10 Proceedings of the 6th international conference on Active media technology
On the relationship between query characteristics and IR functions retrieval bias
Journal of the American Society for Information Science and Technology
Towards an effective automatic query expansion process using an association rule mining approach
Journal of Intelligent Information Systems
Using semantic-based association rule mining for improving clinical text retrieval
HIS'13 Proceedings of the second international conference on Health Information Science
Exploiting semantics for improving clinical information retrieval
Proceedings of the 36th international ACM SIGIR conference on Research and development in information retrieval
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The increasing quantities of available medical resources have motivated the development of effective search tools and medical decision support systems. Medical image search tools help physicians in searching medical image datasets for diagnosing a disease or monitoring the stage of a disease given previous patient's image screenings. Image retrieval models are classified into three categories: content-based (visual), textual and combined models. In most of previous work, a unique image retrieval model is applied for any user formulated query independently of what retrieval model best suits the information need behind the query. The main challenge in medical image retrieval is to cope the semantic gap between user information needs and retrieval models. In this paper, we propose a novel approach for finding correlations between medical query features and retrieval models based on association rule mining. We define new medical-dependent query features such as image modality and presence of specific medical image terminology and make use of existing generic query features such as query specificity, ambiguity and cohesiveness. The proposed query features are then exploited into association rule mining for discovering rules which correlate query features to visual, textual or combined image retrieval models. Based on the discovered rules, we propose to use an associative classifier that finds the best suitable rule with a maximum feature coverage for a new query. Experiments are performed on Image CLEF queries from 2008 to 2012 where we evaluate the impact of our proposed query features on the classification performance. Results show that combining our proposed specific and generic query features is effective for classifying queries. A comparative study between our classifier, CBA, Naïve Bayes, Bayes Net and decision trees showed that our best coverage associative classifier outperforms existing classifiers where it achieves an improvement of 30%.