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 know-how, AMT 2012, held in Macau, China, in December 2012. The sixty five revised complete papers have been conscientiously reviewed and chosen from a quite a few submissions. The papers are equipped in topical sections on understanding multi-agent platforms, facts mining, ontology mining, net reasoning, social purposes of lively media, human-centered computing, personalization and model, clever electronic artwork and e-learning.

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

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Localizing Program Logical Errors Using Extraction of Knowledge from Invariants. , Rebennack, S. ) SEA 2011. LNCS, vol. 6630, pp. 124–135. Springer, Heidelberg (2011) 17. : Effects of resampling method and adaptation on clustering ensemble efficacy, Online (2011) 18. : On Possibility of Conditional Invariant Detection. C. ) KES 2011, Part II. LNCS, vol. 6882, pp. 214–224. Springer, Heidelberg (2011) 19. : Linkage Learning Based on Local Optima. , Hoang, K. ) ICCCI 2011, Part I. LNCS, vol. 6922, pp.

Experiment 3 applies the hot topic detection method based on the agglomerative hierarchical clustering algorithm [4], and topics are evaluated by the total number of posts and replies. As shown in Figure 3, the performance of the proposed method is good. The agglomerative hierarchical clustering algorithm is based on the vector space model, and most of blog posts are not normalized so that the accuracy of the topic detection method based on agglomerative hierarchical clustering algorithm is low.

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. The objective of clustering is to partition a set of unlabeled objects into homogeneous groups or clusters [3], [4] and [10]. There are many applications that use clustering techniques to discover latent structures of data, such as data mining [11], information retrieval [2], image segmentation [9], linkage learning [15], and machine learning.

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