Discovering Optimized Association Rules based on Image Content
Authors present the concept of image mining, an extension of data mining for discovering semantically meaningful information and image data relationship from a large collection of images. Association rule mining is the process of discovering useful and interesting rules, representing frequent patterns from large datasets, depends on user specified minimum support and confidence values. These constraints lead to exponential search space and dataset dependent minimum support and confidence values. The authors propose an optimization technique for overcoming these problems using multi-fitness function Genetic algorithm and constrained nonlinear minimization and minimax optimization method. Synthetic image set containing geometric shapes and standard MIAS medical image dataset are used to validate the proposed optimization algorithm.
Experimental results show that, Genetic algorithm generates more efficient, effective and strong association rules than constrained nonlinear minimization and minimax optimization method. Genetic algorithm achieves 50% and 90%, constrained nonlinear minimization and minimax optimization method achieves 22% and 74 %, reduction in association rules for synthetic image set and standard MIAS medical image dataset respectively.
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