By Yin-Fu Huang, Shih-Hao Wang (auth.), Runhe Huang, Ali A. Ghorbani, Gabriella Pasi, Takahira Yamaguchi, Neil Y. Yen, Beijing Jin (eds.)
This ebook constitutes the refereed court cases of the eighth overseas 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 various submissions. The papers are prepared in topical sections on information multi-agent platforms, information mining, ontology mining, net reasoning, social purposes of lively media, human-centered computing, personalization and version, shrewdpermanent electronic paintings and e-learning.
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This publication constitutes the refereed complaints of the eighth foreign convention on lively Media know-how, AMT 2012, held in Macau, China, in December 2012. The sixty five revised complete papers have been rigorously reviewed and chosen from a a variety of submissions. The papers are equipped in topical sections on knowledge multi-agent platforms, facts mining, ontology mining, internet reasoning, social functions of lively media, human-centered computing, personalization and variation, shrewdpermanent electronic paintings and e-learning.
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Extra info for Active Media Technology: 8th International Conference, AMT 2012, Macau, China, December 4-7, 2012. Proceedings
After selecting a subsett of clusters, to extract the finall partition from them, the real number of clusters is serrved by the consensus functions. As it is known in fuzzy k-means clustering algorithm, each data point belongss to all clusters with different membership m values. To extract the final partition from ooutput of fuzzy k-means algorrithm as consensus function, each data point is assignedd to the most membership valuee. As it is inferred from the Fig. 4, the best ratio of selection of the stable clusterrs is 60% and the best option forr consensus function is CSPA for Iris dataset.
Cluster Ensemble Methods: from Single Clusterings to Combined Solutions. SCI, vol. 126, pp. 3–30 (2008) 9. : A Robust Competitive Clustering Algorithm with Applications in Computer Vision. IEEE Trans. Pattern Analysis and Machine Intelligence 21(5), 450–466 (1999) 10. : Data clustering: A review. ACM Computing Surveys 31(3), 264–323 (1999) 11. : Large-Scale Parallel Data Clustering. IEEE Trans. Pattern Analysis and Machine Intelligence 19(2), 153–158 (1997) A Clustering Ensemble Based on a Modified Normalized Mutual Information Metric 41 12.
Springer, Heidelberg (2011) 32. : A density-based algorithm for discovering clusters in large spatial databases with noise. In: International Conference on Knowledge Discovery and Data Mining, pp. 226–231. AAAI Press (1996) 33. : SLINK: an optimally efficient algorithm for the single-link cluster method. jp Abstract. Side information such as pairwise constraints is useful to improve the clustering performance in general. However, constraints are not always error free in general.