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[10] E. Deelman, G. Singh, M. Livny, B. Berriman, and J. Good, "The Cost of Doing Science on the Cloud: the Montage example," in ACM/IEEE Conference on Supercomputing, Austin, Texas, pp. 1-12, 2008. [11] I. Foster, Z. Yong, I. Raicu, and S. Lu, "Cloud Computing and Grid Computing 360-Degree Compared," in Grid Computing Environments Workshop, GCE '08, pp. 1-10, 2008. [12] S. Ghemawat, H. Gobioff, and S.-T. Leung, "The Google file system," SIGOPS Oper. Syst. Rev., vol. 37, pp. 29-43, 2003. [13] C. Hoffa, G. Mehta, T. Freeman, E. Deelman, K. Keahey, B. Berriman, and J. Good, "On the Use of Cloud Computing for Scientific Workflows," in 4th IEEE International Conference on e- Science, pp. 640-645, 2008. [14] K. Keahey, R. Figueiredo, J. Fortes, T. Freeman, and M. Tsugawa, "Science Clouds: Early Experiences in Cloud Computing for Scientific Applications," in First Workshop on Cloud Computing and its Applications (CCA'08), pp. 1-6, 2008. [15] D. Kondo, B. Javadi, P. Malecot, F. Cappello, and D. P. Anderson, "Cost-benefit analysis of Cloud Computing versus desktop grids," in IEEE International Symposium on Parallel & Distributed Processing, IPDPS'09, pp. 1-12, 2009. [16] B. Ludascher, I. Altintas, C. Berkley, D. Higgins, E. Jaeger, M. Jones, and E. A. Lee, "Scientific workflow management and the Kepler system," Concurrency and Computation: Practice and Experience, pp. 1039–1065, 2005. [17] W. T. McCormick, P. J. Sehweitzer, and T. W. White, "Problem Decomposition and Data Reorganization by a Clustering Technique," Operations Research, vol. 20, pp. 993-1009, 1972. [18] C. Moretti, J. Bulosan, D. Thain, and P. J. Flynn, "All-Pairs: An Abstraction for Data-Intensive Cloud Computing," in IEEE International Parallel & Distributed Processing Symposium, IPDPS'08, pp. 1-11, 2008. [19] L. J. Osterweil, L. A. Clarke, A. M. Ellison, R. Podorozhny, A. Wise, E. Boose, and J. Hadley, "Experience in Using A Process Language to Define Scientific Workflow and Generate Dataset Provenance," in 16th ACM SIGSOFT International Symposium on Foundations of Software Engineering Atlanta, Georgia: ACM, 2008. [20] A. S. Szalay and J. Gray, "Science in an Exponential World," Nature, vol. 440, pp. 23-24, 2006. [21] L. Wang, J. Tao, M. Kunze, A. C. Castellanos, D. Kramer, and W. Karl, "Scientific Cloud Computing: Early Definition and Experience," in 10th IEEE International Conference on High Performance Computing and Communications, HPCC '08. , pp. 825-830, 2008. [22] A. Weiss, "Computing in the Cloud," ACM Networker, vol. 11, pp. 18-25, 2007. 72
HC_AB: A New Heuristic Clustering Algorithm based on Approximate Backbone * Presenter 1. Victoria University Yu Zong 1 * , Guandong Xu 1 , Yanchun Zhang 1 , Mingchu Li 1 Clustering is an important research area with numerous applications in pattern recognition, machine learning, and data mining. Since the clustering problem on numeric data sets can be formulated as a typical combinatorial optimization problem, many research studies have addressed the design of heuristic algorithms for finding local suboptimal solutions in a reasonable period of time. However, a majority of heuristic clustering algorithms suffer from the problem of being sensitive to the initialization and not guaranteeing the high quality clusters. Recently, Approximate Backbone (AB), i.e., the commonly shared intersections of several local suboptimal solutions, has been proposed in heuristic algorithm design to improve the performance of heuristic algorithms. In this paper, we aim to introduce the AB into heuristic clustering to overcome the drawbacks of conventional heuristic clustering algorithms. The main strength of the proposed method is the capability of restricting the initial search space around the global optimal results by defining the AB, and in turn, reducing the impact of initialization on clustering, eventually improving the performance of heuristic clustering. Experiments on synthesis and real world data sets are performed to validate the effectiveness of the proposed approach. 73
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The 3 rd VACPS Research Workshop Pr
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Organizing Committee Victorian Asso
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Preface On the occasion of the publ
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Paper as a low-cost base material f
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Acknowledgements We gratefully ackn
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Career planning and Job Interview i
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The role of mitochondria in atrial
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Track-Before-Detect Procedures for
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3. Dynamic model for approaching ta
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⎧π0 if j = i and Cx ij k > δij
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k k k k ( ) p( p Ζ | X , B = ∏ z
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As illustrated in Fig.4, the struct
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( xˆ , m ˆ ) = argmax Γ ( x , m
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Page 34 and 35: Acknowledgments Xiaobo Deng would l
Page 36 and 37: speed, several methods can be utili
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Page 42 and 43: Demonstration and Performance Analy
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Page 47 and 48: 2.1 Materials Sprague-Dauley(SD) ma
Page 49 and 50: Fig. 2. The representative photomic
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Page 55 and 56: 9.Du XJ, Gao X, Ramsey D. Surgical
Page 57 and 58: Distribution of three forms alpha-m
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Page 61 and 62: Fig.1 (A) Mass Spectrometry results
Page 63 and 64: indicated that there is more des-α
Page 65 and 66: Fig.6 (A) Mass Spectrometry results
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Page 69 and 70: Investigation of the mechanism of t
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Page 84 and 85: A Multi-modal Gesture Recognition S
Page 86: Searching for Fair Joint Gains in A
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Page 101 and 102: Fig. 3 The optical and SEM microstr
Page 103 and 104: Conclusions (1) The large grains in
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Page 125 and 126: new tissue[10]. Most of these human
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studies have indicated that surface
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Fig 10 Tensile Stress curve of elec
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well as on a usual nanofibrous scaf
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Uniform Coating of WO x on TiO 2 Na
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Union as a Social Regulator of Mark
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associated with a reinvention of in
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enterprises…There obviously is no
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Union assistants participate in org
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[5] Deyo, Frederic C and Ağartan,
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Organizational Capabilities • Lea
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Table 1. Correlations, Reliabilitie
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[18]. Jap, S. D. (1999). Pie-expans
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Early Childhood Education Matters:
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decisions on selection of child car
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Noble, K. (2007). Parent choice of
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An Exploration of Country of Origin
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Supercontinuum generation for fiber
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Gas chromatographic retention indic
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Outward Foreign Direct Investment f
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Fiber Nonlinearity Precompensation
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Improved Nonlinearity precompensati
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