A Validity Measure for Fuzzy Clustering
IEEE Transactions on Pattern Analysis and Machine Intelligence
ACM Computing Surveys (CSUR)
Analysis of recommendation algorithms for e-commerce
Proceedings of the 2nd ACM conference on Electronic commerce
Effective personalization based on association rule discovery from web usage data
Proceedings of the 3rd international workshop on Web information and data management
Learning and inferring a semantic space from user's relevance feedback for image retrieval
Proceedings of the tenth ACM international conference on Multimedia
Latent Class Models for Collaborative Filtering
IJCAI '99 Proceedings of the Sixteenth International Joint Conference on Artificial Intelligence
A Hybrid Approach for User Profiling
HICSS '03 Proceedings of the 36th Annual Hawaii International Conference on System Sciences (HICSS'03) - Track 4 - Volume 4
Learning in Content-Based Image Retrieval
ICDL '02 Proceedings of the 2nd International Conference on Development and Learning
Learning from Incomplete Data
Clustering by competitive agglomeration
Pattern Recognition
Learning in region-based image retrieval
CIVR'03 Proceedings of the 2nd international conference on Image and video retrieval
The possibilistic C-means algorithm: insights and recommendations
IEEE Transactions on Fuzzy Systems
IEEE Transactions on Fuzzy Systems
A learning state-space model for image retrieval
EURASIP Journal on Applied Signal Processing
A novel method for image retrieval using relevance feedback and unsupervised clustering
COMPUTE '11 Proceedings of the Fourth Annual ACM Bangalore Conference
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In this paper, a learning method is proposed to improve the retrieval process in image databases. This method uses the search transaction logs in the system and user relevance feedback scores to create a semantic space of the image database. The semantic space includes many semantic classes and all the images in the database are clustered to these classes with different membership values. The sparsity problem in the transaction logs data is solved by filling the missing values by an estimation based on the image contents and image similarities. A Fuzzy clustering algorithm is developed to create the semantic classes and find image memberships in the classes.