參考文獻 |
[1] SDSS: SLOAN digital sky survey, http://www.sdss.org (2012).
[2] Pan-STARRS: The panoramic survey telescope and rapid response system, http://pan-STARRS.ifa.hawaii.edu (2009).
[3] I. Stoica, R. Morris, D. Karger, M. F. Kaashoek, H. Balakrishnan, Chord: A scalable peer-to-peer lookup service for internet applications, SIGCOMM Comput. Commun. Rev. 31 (4) (2001) 149-160.
[4] A. S. Szalay, J. Gray, G. Fekete, P. Z. Kunszt, P. Kukol, A. Thakar, Indexing the sphere with the hierarchical triangular mesh, CoRR abs/cs/0701164.
[5] H. Drucker, C. J. Burges, L. Kaufman, A. Smola, V. Vapnik, Support vector regression machines, in: Advances in Neural Information Processing Systems, 1997, pp. 155-161.
[6] S. J.A.K., V. J., Least squares support vector machine classifiers, in: Neural Processing Letters, Vol. 9, 1999, pp. 293-300.
[7] N. Cristianini, J. Shawe-Taylor, An Introduction to Support Vector Machines and other kernel-based learning methods, Cambridge University Press, 2000.
[8] R. Ranawana, V. Palade, Multi-classifier systems: Review and a roadmap for developers, International Journal of Hybrid Intelligent Systems 3 (1) (2006) 35-61.
[9] J. A. K. Suykens, J. Vandewalle, Least squares support vector machine classifiers, Neural Processing Letters 9 (3) (1999) 293-300.
[10] S. Ratnasamy, P. Francis, M. Handley, R. Karp, S. Shenker, A scalable content-addressable network, SIGCOMM Comput. Commun. Rev. 31 (4) (2001) 161-172.
[11] A. I. T. Rowstron, P. Druschel, Pastry: Scalable, decentralized object location, and routing for large-scale peer-to-peer systems, in: Proceedings of the IFIP/ACM International Conference on
Distributed Systems Platforms Heidelberg, 2001, pp. 329-350.
[12] unszt, P. Z., Szalay, A. S., Csabai, I., Thakar, A. R., The indexing of the sdss science archive, in: In ASP Conf. Ser., Astronomical Data Analysis Software and Systems IX, 2000, p. 141(216).
[13] Short, N.M., Cromp, R.F., Campbell, W.J., Tilton, J.C., LeMoigne, J., Fekete, G., Netanyahu, N.S., Wichmann, K., Ligon, W.B., Mission to plane earth: Ai views the world, IEEE Expert (1995) 24-34.
[14] F. G., Rendering and managing spherical data with sphere quadtrees, in: Proceedings of Visualization ′90. IEEE Computer Society, 1990, pp. 176-186.
[15] H. Samet, The Design and Analysis of Spatial Data Structures, Addison Wesley, 1989.
[16] H. Samet, Application of Spatial Data Structures, Addison Wesley, 1990.
[17] M. Lee, H. Samet, Navigating through triangle meshes implemented as linear quadtrees, Tech. rep. (1998).
[18] Goodchild, M. F., Y. S. et al., Spatial data representation and basic operations on triangular hierarchical data structure, Tech. rep. (1991).
[19] M. Goodchild, Y. Shiren, A hierarchical data structure for global geographic information systems, in: CVGIP: Graphical Models and Image Processing, 1992, pp. 31-44.
[20] L. Song, Kimerling, A.J., Sahr, K., Developing an equal area global grid by small circle subdivision, in: Proc. International Conference on Discrete Global Grids, 2000, pp. 26-28.
[21] J. Gray, Szalay, A. Fekete, O. M. G., W. Nieto-Santisteban, M.A., Thakar, A.R., Heber, G., Rots, A.H., There goes the neighborhood: Relational algebra for spatial data search, Tech. rep. (2004).
[22] Q.-A. Tran, Q.-L. Zhang, X. Li, Reduce the number of support vectors by using clustering techniques, Machine Learning and Cybernetics 2 (2003) 1245- 1248.
[23] K.Woodsend, J. Gondzio, High-performance parallel support vector machine training, Parallel Scientific Computing and Optimization 27 (2008) 83-92.
[24] D. Hush, C. Scovel, Polynomial-time decomposition algorithms for support vector machines, Machine Learning 51 (1) (2003) 51- 71.
[25] R. Collobert, S. Bengio, Y. Bengio, A parallel mixture of svms for very large scale problems, Neural Computation 14 (5) (2002) 1105-1114.
[26] E. Chang, K. Zhu, H. Wang, H. Bai, J. Li, Z. Qiu, H. Cui, Psvm: Parallelizing support vector machines on distributed computers, In Advances in Neural Information Processing Systems 20.
[27] A. Meligy, M. Al-Khatib, A grid-based distributed svm data mining algorithm, European Journal of Scientific Research 27 (3) (2009) 313-321.
[28] A. K. Jain, M. N. Murty, P. J. Flynn, Data Clustering: a review, ACM Computing Surveys 31(3) (1999) 264-323.
[29] B. Andreopoulos, A. An, X. Wang, M. Schroeder, A Roadmap of Clustering Algorithms: finding a match for a biomedical application, Briefings in Bioinformatics 10 (2009) 297-314.
[30] W. Kim, Parallel Clustering Algorithms: survey (2009).
[31] R. Xu, Survey of clustering algorithms, IEEE Transactions on Neural Networks 16(3) (2005) 645-678.
[32] B. S. Everitt, S. Landau, M. Leese, D. Stahl, Cluster Analysis, 5th edition, Wiley, 2010.
[33] R. Mojena, Hierarchical Grouping Methods and Stopped Rules: an evaluation, Computer Journal 20 (1977) 359-363.
[34] R. Sibson, SLINK: an optimally efficient algorithm for the single-link cluster method, The Computer Journal 16 (1973) 30-34.
[35] E. W. Dijkstra, A note on two problems in connexion with graphs, NUMERISCHE MATHEMATIK 1 (1959) 269-271.
[36] R. C. Prim, Shortest connection networks and some generalizations, The Bell Systems Technical Journal 36 (1957) 1389-1401.
[37] D. Defays, An efficient algorithm for a complete link method, The Computer Journal 20 (1977) 364-366.
[38] J. A. Hartigan, M. A. Wong, Algorithm AS136 a K-means clustering algorithm, Applied Statistics 28 (1979) 100-108.
[39] P. S. Bradley, U. M. Fayyad, Refining initial points for K-means clustering, in: Proceedings of the Fifteenth International Conference on Machine Learning, Morgan kaufmann, 1998, pp. 91-99.
[40] R. Kothari, D. Pitts, On finding the number of clusters, Pattern Recognition Letters 20 (4) (1999) 405 - 416.
[41] T. Ishioka, Extended K-means with an efficient estimation of the number of clusters, in: Proceedings of the Second International Conference on Intelligent Data Engineering and Automated
Learning, 2000, pp. 17-22.
[42] D. T. Pham, S. S. Dimov, C. D. Nguyen, Selection of K in K-means clustering, Mechanical Engineering Science 219 (2004) 103- 119.
[43] A. P. Dempster, N. M. Laird, D. B. Rubin, Maximum likelihood from incomplete data via the EM algorithm, Journal of the Royal Statistical Society, Series B 39 (1) (1977) 1-38.
[44] B. S. Everitt, A. Skrondal, Cambridge Dictionary of Statistics, Cambridge University Press, 2010.
[45] M. Ester, H. peter Kriegel, J. Sander, X. Xu, A density-based algorithm for discovering clusters in large spatial databases with noise, in: Proceedings of 2nd International Conference on Knowl-
edge Discovery and Data Mining, AAAI Press, 1996, pp. 226-231.
[46] M. Ankerst, M. M. Breunig, H. peter Kriegel, J. Sander, OPTICS: ordering points to identify the clustering structure, in: Proceedings of the 1999 ACM SIGMOD International Conference on Man-
agement of Data, Vol. 28, 1999, pp. 49-60.
[47] X. Zhu, X. Wu, Y. Yang, Effiective classification of noisy data streams with attribute-oriented dynamic classifier selection, Knowledge and Information Systems 9 (3) (2006) 339-363.
[48] K. Woods, W. P. K. Jr., K. Bowyer, Combination of multiple classifiers using local accuracy estimates, IEEE Transactions on Pattern Analysis and Machine Intelligence 19 (4) (1997) 405-410.
[49] E. Kim, J. Ko, Dynamic classifier integration method, Multiple Classifier Systems (2005) 97-107.
[50] P. J. Phillips, P. J. Flynn, T. Scruggs, K. W. Bowyer, J. Chang, K. Hoffman, J. Marques, J. Min, W. Worek, Overview of the face recognition grand challenge, in: Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2005, pp. 947-954.
[51] P. Havlak, R. Chen, K. J. Durbin, A. Egan, Y. Ren, X. Z. Song, G. M. Weinstock, R. A. Gibbs, The Atlas genome assembly system, Genom Research 14 (2004) 721-732.
[52] X. Huang, J. Wang1, S. Aluru, S.-P. Yang, L. Hillier, PCAP: a whole-genome assembly program, Genom Research 13 (2003) 2164-2170.
[53] D. Vokrouhlicky, D. Nesvorny, Pairs of asteroids probably of a common origin, The Astronomical Journal 136 (1) (2008) 280.
[54] Z. Vincenzo, C. Alberto, F. Paolo, K. Zoran, Asteroid Families. I - identification by hierarchical clustering and reliability assessment, The Astronomical Journal 100 (1990) 2030-2046.
[55] C. Moretti, H. Bui, K. Hollingsworth, B. Rich, P. Flynn, D. Thain, All-Pairs: an abstraction for data intensive computing on campus grids, IEEE Transactions on Parallel and Distributed Systems 21 (2010) 33-46.
[56] E. Dahlhaus, Parallel algorithms for hierarchical clustering and applications to split decomposition and parity graph recognition, Journal of Algorithms 36 (1998) 2000.
[57] R. M. Karp, V. Ramachandran, in: Handbook of Theoretical Computer Science (Vol. A), MIT Press, 1990, Ch. Parallel Algorithms for Shared-memory Machines, pp. 869-941.
[58] C. F. Olson, Parallel algorithms for hierarchical clustering, Parallel Computing 21 (1995) 1313-1325.
[59] M. Imai, Y. Hayakawa, H. Kawanaka, W. Chen, K. Wada, C. D. Castanho, Y. Okajima, H. Okamoto, A hardware implementation of pram and its performance evaluation, in: Proceedings of the 15 IPDPS 2000 Workshops on Parallel and Distributed Processing, 2000, pp. 143-148.
[60] X. Li, Parallel algorithms for hierarchical clustering and cluster validity, IEEE Transactions on Pattern Analysis and Machine Intelligence 12 (1990) 1088-1092.
[61] M. J. Flynn, Some computer organizations and their effiectiveness, IEEE Transactions on Computers C-21 (1972) 948-960.
[62] C. Wu, S. Horng, H. Tsai, Efficient parallel algorithms for hierarchical clustering on arrays with reconfigurable optical buses, Journal of Parallel and Distributed Computing 60 (2000) 1137- 1153.
[63] E. M. Rasmussen, P. Willett, Efficiency of hierarchical agglomerative clustering using the ICL distributed array processor, Journal of Documentation 45(1) (1989) 1-24.
[64] J. H. Ward, Hierarchical grouping to optimize an objective function, Journal of the American Statistical Association 58(301) (1963) 236-244.
[65] A. Garg, A. Mangla, N. Gupta, V. Bhatnagar, PBIRCH: a scalable parallel clustering algorithm for incremental data, in: Proceedings of the 10th International Database Engineering and Applications Symposium, 2006, pp. 315-316.
[66] T. Zhang, R. Ramakrishnan, M. Livny, BIRCH: an efficient data clustering method for very large databases, in: In Proc. of the ACM SIGMOD Intl. Conference on Management of Data, 1996, pp. 103-114.
[67] D. Talia, Parallelism in knowledge discovery techniques, in: LNCS 2367: Applied Parallel Computing, 6th International Conference PARA′02, 2002, pp. 127-136.
[68] T. Sun, C. Shu, F. Li, H. Yu, L. Ma, Y. Fang, An efficient hierarchical clustering method for large datasets with map-reduce, in: International Conference on Parallel and Distributed Computing, Applications and Technologies, 2009, pp. 494-499.
[69] S.Wang, H. Dutta, PARABLE: a parallel random-partition based hierarchical clustering algorithm for the mapreduce framework, Tech. rep., The Center for Computational Learning Systems (2011).
[70] M. Dash, S. Petrutiu, P. Scheuermann, pPOP: fast yet accurate parallel hierarchical clustering using partitioning, Data and Knowledge Engineering 61 (2007) 563-578.
[71] T. H. Cormen, C. E. Leiserson, R. L. Rivest, C. Stein, Introduction to Algorithms, Second Edition, The MIT Press, 2001.
[72] F. Muhlenbach, S. Lallich, A new clustering algorithm based on regions of influence with self-detection of the best number of clusters, in: Proceedings of the 2009 Ninth IEEE International Conference on Data Mining, 2009, p. 884-889.
[73] The MPC Orbit (MPCORB) Database, http://minorplanetcenter.org/iau/MPCORB.html (2012). |