AI 2011: Advances in Artificial Intelligence: 24th by Mani Abedini, Michael Kirley (auth.), Dianhui Wang, Mark

By Mani Abedini, Michael Kirley (auth.), Dianhui Wang, Mark Reynolds (eds.)

This ebook constitutes the refereed complaints of the twenty fourth Australasian Joint convention on synthetic Intelligence, AI 2011, held in Perth, Australia, in December 2011. The eighty two revised complete papers offered have been rigorously reviewed and chosen from 193 submissions. The papers are geared up in topical sections on information mining and data discovery, computer studying, evolutionary computation and optimization, clever agent structures, common sense and reasoning, imaginative and prescient and portraits, picture processing, usual language processing, cognitive modeling and simulation expertise, and AI applications.

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Additional info for AI 2011: Advances in Artificial Intelligence: 24th Australasian Joint Conference, Perth, Australia, December 5-8, 2011. Proceedings

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Although there have been several research works on clustering in general, most classic data mining algorithms do not work well for time series due to their unique characteristics. In particular, the high dimensionality not only slows the clustering process but also degrades it. The k-Means is one of the popular algorithms for clustering time series data ([11], [8]). However, k-Means still suffers one shortcoming: the initial cluster centers are still chosen randomly and hence the clustering quality depends significantly on these initial centers.

On Knowledge Discovery and Data Mining (KDD 2003), pp. 493–498 (2003) 3. : Stock temporal prediction based on time series motifs. In: Proc. of 8th Int. Conf. on Machine Learning and Cybernetics, Baoding, July 12-15 (2009) 4. : Dimensionality reduction for fast similarity search in large time series database. Journal of Knowledge and Information Systems, 263–286 (2000) 5. html 6. : Motif-based method for k-Means clustering of time series data. Master Thesis, Faculty of Computer Science & Engineering, Ho Chi Minh City University of Technology, Vietnam (June 2011) 7.

Unlike other neighborhood-based schemes, our classifier uses only a small set of representatives whose cardinality can be much smaller than that of the input set. Our experiments show that, on average, the accuracy of such classifier is reasonably comparable to those obtained by some of the state-of-the-art classification schemes that only use labeled instances during the training phase. The experiments also show that improved levels of accuracy can be obtained by imposing trees with a larger number of nodes.

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