Reference: | [1] J. Han, M. Kamber, J. Pei, (2011). “Data mining: concepts and techniques”, Burlington, Massachusetts, USA: Morgan Kaufmann. [2] P. Tan, M. Steinbach, and V. Kumar, (2005). “Introduction to data mining”, Boston, MA, USA: Addison-Wesley Longman Publishing Co., Inc. [3] A. K. Jain, M. N. Murty, and P. J. Flynn, (1999). “Data clustering: A review”, ACM computing surveys (CSUR), vol. 31, no. 3, pp. 264–323. [4] C. Aggarwal and K. Reddy, (2013). “Data clustering: Algorithms and applications”, UK: Chapman & Hall/CRC. [5] M. Steinbach, G. Karypis, V. Kumar et al., (2000). “A comparison of document clustering techniques,” KDD workshop on text mining, vol. 400, no. 1, pp. 525–526. [6] D. L. Pham, (2001). “Spatial models for fuzzy clustering”, Computer vision and image understanding, vol. 84, no. 2, pp. 285–297. [7] S. Kotsiantis, (2007). “Supervised machine learning: A review of classification techniques”, Informatica Journal, no. 31, pp. 249–268. [8] S. Haykin, (1998). “Neural networks: A comprehensive foundation”, Upper Saddle River, New Jersey, USA: Upper Saddle River: Prentice Hall. [9] M. Seeger, (2001). “Learning with labeled and unlabeled data.”, Ottawa-Carleton Institute for Computer Science. [10] Hinton, Geoffrey, Sejnowski, Terrence, (1999). “Unsupervised learning: foundations of neural computation”, Boston, MA, USA: MIT Press. [11] J. Buhmann, H. Kuhnel, (1992). “Unsupervised and supervised data clustering with competitive neural networks”, IJCNN International Joint Conference on Neural Networks. 4. pp. 796–801. [12] N. Grira, M. Crucianu, N. Boujemaa, (2004). “Unsupervised and semi-supervised clustering: A brief survey”, A Review of Machine Learning Techniques for Processing Multimedia Content, Report of the MUSCLE European Network of Excellence. [13] X. Zhu, A. B. Goldberg, (2009). “Introduction to semi-supervised learning”, UK: CRC. [14] S. Basu, A. Banerjee, R. J. Mooney, (2002). “Semi-supervised clustering by seeding”, in: Proceedings of ICML, pp. 27–34. [15] S. Basu, A. Banerjee, R. J. Mooney, (2004). “Active semi-supervision for pairwise constrained clustering”, in: Proceedings of SIAM Data Mining, pp. 333–344. [16] Z. Sun, G. Fox, W. Gu, (2014). “A parallel clustering method combined information bottleneck theory and centroid-based clustering”, The Journal of Supercomputing, no.69, pp. 452-467. [17] F. Gullo, A. Tagarelli, (2012). “Uncertain centroid based partitional clustering of uncertain data”, Scalable Uncertainly Management, pp. 229-242. [18] A. Solovyov, W. L. Lipkin, (2013). “Centroid based clustering of high throughput sequencing reads based on n-mer counts”, BMC Bioinformatics, no.268. [19] R. Sibson, (1973). “An optimally efficient algorithm for the single-link cluster method”, The Computer Journal. British Computer Society. [20] F. Murtagh, P. Contreras, (2012). “Algorithms for hierarchical clustering: An overview”, WIREs Data Mining and Knowledge Discovery, vol.2. [21] A. P. Reynolds, G. Richards, B. de la lglesia, V. J. Rayward-Smith, (2006). “Clustering rules: A comparison of partitioning and hierarchical clustering algorithms”, Journal of Mathematical Modelling and Algorithms, vol. 5, pp. 475–504. [22] E. Martin, K. Hans-Peter, J. Sander, X. Xu, (1996). “A Density-Based Algorithm for Discovering Clusters in Large Spatial Databases with Noise,” Proceedings of the Second International Conference on Knowledge Discovery and Data Mining(KDD-96). [23] H. Kriegel, P. Kroger, J. Sander, A. Zimek, (2011). “Density-based clustering”. [24] McInnes et al, (2017), “Hierarchical density based clustering”, Journal of Open Source Software, vol.2, no.11, pp. 205. [25] X. Xu, M. Ester, H. Kriegel, J. Sander, (1998). “A distribution-based clustering algorithm for mining in large spatial databases”, Proceedings of the Fourteenth International Conference on Data Engineering, pp. 324-331. [26] M. Bendechache, M. Kechadi, (2018). “Distributed Clustering Algorithm for Spatial Data mining”, Ireland: School of Computer Science & Informatics. [27] R. Corizzo, G. Pio, M. Ceci, et al. (2019). “DENCAST: distributed density-based clustering for multi-target regression”, Journal of Big Data, vol.6, no.43. [28] J. B. MacQueen, (1967). “Some methods for classification and analysis of multivariate observations”, in Proceedings of the Fifth Berkeley Symposium on Mathematical Statistics and Probability, vol.1, pp.281-297. [29] J. A. Hartigan, M. A. Wong, (1979). “Algorithm AS 136: A K-means clustering algorithm”, Journal of the Royal Statistical Society, Series C, vol.28, no.1, pp.100-108. [30] K. L. Wu, Y. J. Lin, (2012). “Kernelized K-means algorithm based on Gaussian kernel”, Advances in Control and Communication, pp 657-664. [31] Y. Zhao, S. Zhang, J. Ma, (2013). “Kernel K-means algorithm for clustering analysis”, Intelligent Computing Theories and Technology. pp. 234-243. [32] I. S. Dhillon, Y. Guan, B. Kulis, (2004). “Kernel K-means, spectral clustering and normalized cuts”, Research Tracker Poster, pp.551-556. [33] N. Ganganath, C. Cheng, and C. K., (2014). “Data clustering with cluster size constraints using a modified K-means algorithm”, Cyber-Enabled Distributed Computing and Knowledge Discovery. [34] P.S. Bradley, K.P. Bennett, A. Demiriz, (2000). “Constrained K-means clustering”, Technical Report MSR-TR-2000-65, Microsoft Research. [35] M. Baranwal and S. M. Salapaka, (2017). “Clustering with capacity and size constraints: A deterministic approach”, ICC. [36] S.Lloyd, (1982). “Least square quantization in PCM,” IEEE Transations on Information Theory, vol.28, no. 2, pp. 129-137. [37] S. Zhu, D. Wang, and T. Li, (2010). “Data clustering with size constraints,” Knowledge-Based Systems, vol. 23, no. 8, pp. 883–889. [38]林育丞 (2019)。《利用資料驅動方法解決汽車服務系統的位區途程問題》。國立政治大學,統計所,臺北。 [39] H. Massatfa, (1992). “An algorithm to maximize the agreement between partitions”, Journal of Classification 9, vol.1, pp.5–15. [40] L. Kaufman and P.J. Rousseeuw, (1987). “Clustering by means of medoids”, Amsterdam: North-Holland. [41] E. Schubert and P. Rousseeuw, (2019). “Faster K-medoids clustering: Improving the PAM, CLARA, and CLARANS algorithms”, SISAP 2019: Similarity Search and Applications, pp. 171-187. [42] C. Lemarecha, (2012). “Cauchy and the gradient method”, Documenta Mathematica Extra Volume ISMP, pp. 251-254. [43] E. Polak, (1997). “Optimization : Algorithms and consistent approximations”, New York: Springer-Verlag. [44] COIN-OR/SYMPHONY, Retrieved 2006, from: https://github.com/coinor/SYMPHONY [45] Greenberg, (1997). “Klee-Minty Polytope Shows Exponential Time Complexity of Simplex Method.”, University of Colorado at Denver. [46] A. Arias, J. D. Sanchez, and M. Granada, (2018). “Integrated planning of electric vehicles routing and charging stations location considering transportation networks and power distribution systems”, International Journal of Industrial Engineering Computations, vol. 9, no. 4, pp. 535-550. [47] R. T. Berger, C. R. Coullard, and M. S. Daskin, (2017). “Location-routing problems with distance constraints”, Transportation Science, vol. 41, pp. 29-43. [48] T.W. Chien, (1993). “Heuristic procedures for practical-sized uncapacitated location-capacitated routing problems”, Decision Sciences, vol.24, pp.995-1021. [49] J. Hof, M. Schneider, and D. Goeke, (2017). “Solving the battery swap station location-routing problem with capacitated electric vehicles using an AVNS algorithm for vehicle-routing problems with intermediate stops”, Transportation Research Part B Methodological, vol.97, pp.102-112. [50] Y. C. Hung and G. Michailidis, (2015). “Optimal routing for electric vehicle service systems”, European Journal of Operational Research, vol. 247, no.2, pp.515- 524. [51] J. Paz, M. Granada-Echeverri, and J. Escobar, (2018). “The multi-depot electric vehicle location routing problem with time windows”, International Journal of Industrial Engineering Computations, vol.9, no.1, pp. 123-136. [52] J. Yang and H. Sun, (2015). “Battery swap station location-routing problem with capacitated electric vehicles”, Computers and Operations Research, vol. 55, no. C, pp. 217-232. [53] L. Wang and Y. B. Song, (2015). “Multiple charging station location-routing problem with time window of electric vehicle”, Journal of Engineering Science and Technology Review, vol. 8, no. 5, pp. 190-201. [54] D. Efthmiou, K. Chrysostomou, M. Morfoulaki, and G. Aifantopoulou, (2017). “Electric vehicles charging infrastructure location: a genetic algorithm approach”, European Transport Research Review, vol.9, no.27. [55] Q. Kong, M. Fowler, E. Entchev, H. Ribberink, and R. McCallum, (2018). “The role of charging infrastructure in electric vehicle implementation within smart grids”, Energies, vol.11, no.3362. [56] S. Deb, K. Kalita, and P. Mahanta, (2018). “Impact of electric vehicle charging station load on distribution network,” Energies, vol. 11, no. 178. [57] W. Yuan, J. Huang, Y. Jun, and A. Zhang, (2017). “Competitive charging station pricing for plug-in electric vehicles,” IEEE Transactions on Smart Grid, vol. 8, no. 2, pp.627-639. [58] Y. Marinakis, (2008). “Location routing problem. In: Floudas C., Pardalos P. (eds) encyclopedia of optimization.”, Boston, MA, USA: Springer. |