Download Advances in Knowledge Discovery and Data Mining: 18th by Vincent S. Tseng, Tu Bao Ho, Zhi-Hua Zhou, Arbee L.P. Chen, PDF

By Vincent S. Tseng, Tu Bao Ho, Zhi-Hua Zhou, Arbee L.P. Chen, Hung-Yu Kao

The two-volume set LNAI 8443 + LNAI 8444 constitutes the refereed lawsuits of the 18th Pacific-Asia convention on wisdom Discovery and knowledge Mining, PAKDD 2014, held in Tainan, Taiwan, in may possibly 2014. The forty complete papers and the 60 brief papers provided inside those lawsuits have been conscientiously reviewed and chosen from 371 submissions. They disguise the final fields of trend mining; social community and social media; type; graph and community mining; functions; privateness holding; suggestion; characteristic choice and relief; laptop studying; temporal and spatial facts; novel algorithms; clustering; biomedical info mining; movement mining; outlier and anomaly detection; multi-sources mining; and unstructured info and textual content mining.

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Additional info for Advances in Knowledge Discovery and Data Mining: 18th Pacific-Asia Conference, PAKDD 2014, Tainan, Taiwan, May 13-16, 2014. Proceedings, Part I

Example text

Moreover, the work in [15] has focused on comparing the data sets using diverse association rules. In this paper, we developed a framework to compute the diversity of patterns by analyzing the categories of items. The rest of the paper is organized as follows. In the next section, we explain about concept hierarchy and diversity of pattern. In section 3, we explain the approach to computing the drank of a pattern by considering balanced concept hierarchy. In section 4, we present the proposed approach.

Relatively, if the items are mapped to multiple categories, we consider that the pattern has more diversity. We have developed an approach to assign the diversity for a given pattern based on the merging behavior in the corresponding concept hierarchy. If the pattern merges into few higher level categories quickly, it has low diversity. Otherwise, if the pattern merges into one or a few high level categories slowly, it has relatively high diversity value. As an example, consider the concept hierarchy in Figure 1.

An example of balanced concept hierarchy Let C be a concept hierarchy. A node in C may be an item, category or root. The height of root node is 0. Let n be a node in C. The height of n, is denoted as h(n), is equal to the number of edges on the path from root to n. Figure 1 represents a concept hierarchy. In this, the items orange, apple and cherry are mapped to the category fruits. Similarly, the categories drinks and fruits are mapped to the category fresh food. Finally, the categories fresh food and house hold are mapped to root.

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