Active Media Technology: 8th International Conference, AMT by Yin-Fu Huang, Shih-Hao Wang (auth.), Runhe Huang, Ali A.

By Yin-Fu Huang, Shih-Hao Wang (auth.), Runhe Huang, Ali A. Ghorbani, Gabriella Pasi, Takahira Yamaguchi, Neil Y. Yen, Beijing Jin (eds.)

This booklet constitutes the refereed court cases of the eighth foreign convention on energetic Media expertise, AMT 2012, held in Macau, China, in December 2012. The sixty five revised complete papers have been conscientiously reviewed and chosen from a a variety of submissions. The papers are prepared in topical sections on wisdom multi-agent platforms, info mining, ontology mining, net reasoning, social purposes of energetic media, human-centered computing, personalization and variation, shrewdpermanent electronic paintings and e-learning.

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Additional info for Active Media Technology: 8th International Conference, AMT 2012, Macau, China, December 4-7, 2012. Proceedings

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As for that phenomenon, the minimum of two theme models is also taken into consideration by using the following equation, in order to correctly compare the similarity between theme models: |di ∩ dj | |di ∩ dj | +β∗ (2) sim(di , dj ) = α ∗ |di ∪ dj | min(|di |, |dj |) where dj denotes the keyword set of the jth theme, Sim(di , dj ) is the similarity between the ith theme and the jth theme, di ∩ dj is the intersection between di and dj , di ∪ dj is the union of di and dj , |di | is the size of set di , α and β are coefficients, and min(y, z) is the minimum between y and z.

To aggregate the final partitionings into consensus partitioning, a number of well-known methods are employed to make a decisive conclusion. Rest of this paper is organized as follows. In section 2, we explain the proposed method. Section 3 demonstrates results of our proposed method against traditional comparatively. Finally, we conclude in section 4. Fig. 1. Clustering Ensemble Framework 2 Proposed Method In this section, first our proposed clustering ensemble method is briefly outlined, and then its phases are described in detail.

Classifier ensemble as a sub-field of ensemble learning is a general method to improve the performance of classification. At first glance, usage of ensemble learning in clustering sounds similar to the widely prevalent use of combining multiple classifiers to solve difficult classification problems, using techniques such as bagging and boosting. R. Huang et al. ): AMT 2012, LNCS 7669, pp. 32–42, 2012. © Springer-Verlag Berlin Heidelberg 2012 A Clustering Ensemble Based on a Modified Normalized Mutual Information Metric 33 Data clustering or unsupervised learning is an important and very difficult problem.

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