Mining frequent patterns in image databases with 9D-SPA representation |
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Authors: | Anthony JT Lee [Author Vitae] Ying-Ho Liu [Author Vitae] [Author Vitae] Hsiu-Hui Lin [Author Vitae] [Author Vitae] |
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Affiliation: | Department of Information Management, National Taiwan University, No. 1, Sec. 4, Roosevelt Road, Taipei 10617, Taiwan, ROC |
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Abstract: | In this paper, we propose a novel algorithm, called 9DSPA-Miner, to mine frequent patterns from an image database, where every image is represented by the 9D-SPA representation. Our proposed method consists of three phases. First, we scan the database once and create an index structure. Next, the index structure is scanned to find all frequent patterns of length two. Finally, we use the frequent k-patterns (k ? 2) to generate candidate (k + 1)-patterns and check if the support of each candidate generated is not less than the user-specified minimum support threshold by using the index structure. Then, the steps in the third phase are repeated until no more frequent patterns can be found. Since the 9DSPA-Miner algorithm uses the characteristics of the 9D-SPA representation to prune most of impossible candidates, the experiment results demonstrate that it is more efficient and scalable than the modified Apriori method. |
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Keywords: | Data mining Spatial association rules Spatial frequent patterns 9D-SPA representation |
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