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Journal of Computer Science and Technology ›› 2019, Vol. 34 ›› Issue (5): 1152-1166.doi: 10.1007/s11390-019-1966-0
Special Issue: Data Management and Data Mining
• Data Management and Data Mining • Previous Articles
Da-Wei Wang1, Wan-Qiu Cui2, Biao Qin1,*, Member, CCF
[1] Shiokawa H, Fujiwara Y, Onizuka M. SCAN++:Efficient algorithm for finding clusters, hubs and outliers on largescale graphs. Proceedings of the VLDB Endowment, 2015, 8(11):1178-1189. [2] Zhang W P, Li Z J, Li R H, Liu Y H, Mao R, Qiao S J. MapReduce-based graph structural clustering algorithm. Journal of Software, 2018, 29(3):627-641. (in Chinese) [3] Wu Y, Zhong Z N, Xiong W, Chen L, Jing N. An efficient method for attributed graph clustering. Chinese Journal of Computer, 2013, 36(8):1704-1713. (in Chinese) [4] Guo T, Ding X W, Li Y F. Parallel K-modes algorithm based on MapReduce. In Proc. the 3rd International Conference on Digital Information, Networking, and Wireless Communications, February 2015, pp.176-179. [5] Zhou F F, Li J C, Huang W, Wang J H, Zhao Y. Extending dimensions in Radviz for visual clustering analysis. Journal of Software, 2016, 27(5):1127-1139. (in Chinese) [6] Noori-Daryan M, Taleizadeh A A, Govindan K. Joint replenishment and pricing decisions with different freight modes considerations for a supply chain under a composite incentive contract. Journal of the Operational Research Society, 2018, 69(6):876-894. [7] Huang Z X. Clustering large data sets with mixed numeric and categorical values. In Proc. the 1st Pacific-Asia Conference on Knowledge Discovery and Data Mining, February 1997, pp.21-35. [8] Ahmad A, Dey L. A method to compute distance between two categorical values of same attribute in unsupervised learning for categorical data set. Pattern Recognition Letters, 2007, 28(1):110-118. [9] Park H S, Jun C H. A simple and fast algorithm for Kmedoids clustering. Expert Systems with Applications, 2009, 36(2):3336-3341. [10] Zadegan S M R, Mirzaie M, Sadoughi F. Randed Kmedoids:A fast and accurate rank-based partitioning algorithm for clustering large datasets. Knowledge-Based Systems, 2013, 39:133-143. [11] Ferrarini L, Olofsen H, Palm W M, van Buchem M A, Reiber J H C, Admiraal-Behloul F. GAMEs:Growing and adaptive meshes for fully automatic shape modeling and analysis. Medical Image Analysis. 2007, 11(3):302-314. [12] Ng M K, Chan E Y, So M M C, Ching W K. A semisupervised regression model for mixed numerical and categorical variables. Pattern Recognition, 2007, 40(6):1745-1752. [13] Bachem O, Lucic M, Hassani S H, Krause A. Approximate K-means++ in sublinear time. In Proc. the 30th AAAI Conference on Artificial Intelligence, February 2016, pp.1459-1467. [14] Arthur D, Vassilvitskii S. K-means++:The advantages of careful seeding. In Proc. the 18th Annual ACM-SIAM Symposium on Discrete Algorithms, January 2007, pp.1027-1035. [15] Liu Y C, Li Z M, Xiong H, Gao X D, Wu J J. Understanding of internal clustering validation measures. In Proc. the 10th IEEE International Conference on Data Mining, December 2010, pp.911-916. [16] Liu Y C, Li Z M, Xiong H, Gao X D, Wu J J. Understanding and enhancement of internal clustering validation measures. IEEE Transactions on Cybernetics, 2013, 43(3):982-994. [17] Robinson I, Webber J, Eifrem E. Graph Databases (1st edition). O'Reilly Media, 2013. [18] Akpan N P, Iwok I A. A minimum spanning tree approach of solving a transportation problem. International Journal of Mathematics and Statistics Invention, 2017, 5(3):9-18. [19] Li M C, Han S, Shi J. An enhanced ISODATA algorithm for recognizing multiple electric appliances from the aggregated power consumption dataset. Energy and Buildings, 2017, (140):305-316. [20] Hesthaven J S. A stable penalty method for the compressible Navier-Stokes equations:Ⅱ. One-dimensional domain decomposition schemes. SIAM Journal on Scientific Computing, 1997, 18(3):658-685. [21] Jin X, Han J. K-medoids clustering. In Encyclopedia of Machine Learning, Sammut G, Webb G I (eds.), Springer, 2016, pp.564-565. [22] Han L S, Xiang L S, Liu X Y, Luan J. The K-medoids algorithm with initial centers optimized based on a P System. Journal of Information and Computational Science, 2014, 11(6):1765-1773. [23] Kang Z, Peng C, Cheng Q. Clustering with adaptive manifold structure learning. In Proc. the 33rd Int. Conference on Data Engineering, Apr. 2017, pp.79-82. [24] Nehak D, Dehak R, Glass J, Reynolds D, Kenny P. Cosine similarity scoring without score normalization techniques. In Proc. the Speaker and Language Recognition Workshop, June 2010, Article No. 15. [25] Cheng H, Zhou Y, Yu J X. Clustering large attributed graphs:A balance between structural and attribute similarities. ACM Transactions on Knowledge Discovery from Data, 2011, 5(2):Article No. 12. [26] Chang L J, Li W, Lu Q, Zhang W J, Yang S Y. pSCAN:Fast and exact structural graph clustering. IEEE Transactions on Knowledge and Data Engineering, 2017, 29(2):387-401. [27] Schubert E, Sander J, Ester M, Kriegel H P, Xu X W. DBSCAN revisited, revisited:Why and how you should (still) use DBSCAN. ACM Transactions on Database Systems, 2017, 42(3):Article No. 19. [28] Du Z H, Li Y B. An improved BIRCH clustering algorithm and application in thermal power. In Proc. the 2010 International Conference on Web Information Systems and Mining, October 2010, pp.53-56. [29] Xiong H, Wu J J, Chen J. K-means clustering versus validation measures:A data-distribution perspective. IEEE Transactions on Systems, Man, and Cybernetics, Part B, 2009, 39(2):318-331. [30] Wu J J, Xiong H, Chen J. Adapting the right measures for K-means clustering. In Proc. the 15th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, June 2009, pp.877-886. |
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