document: From frequent itemsets to semantically meaningful visual patterns Data mining techniques that are successful in transaction and text data may not be simply applied to image data that contain high-dimensional features and have spatial structures . It is not a trivial task to discover meaningful visual patterns in image databases , because the content variations and spatial dependency in the visual data greatly challenge most existing methods . This paper presents a novel approach to coping with these difficulties for mining meaningful visual patterns . Specifically , the novelty of this work lies in the following new contributions : ( 1 ) a principled solution to the discovery of meaningful itemsets based on frequent itemset mining ; ( 2 ) a self-supervised clustering scheme of the high-dimensional visual features by feeding back discovered patterns to tune the similarity measure through metric learning ; and ( 3 ) a pattern summarization method that deals with the measurement noises brought by the image data . The experimental results in the real images show that our method can discover semantically meaningful patterns efficiently and effectively .
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