Page 35 - Data Science Algorithms in a Week

20 Ramazan Ünlü

Luo, H., Jing, F., & Xie, X. (2006). Combining multiple clusterings using information
theory based genetic algorithm Computational Intelligence and Security, 2006
International Conference on (Vol. 1, pp. 84-89): IEEE.
MacQueen, J. (1967). Some methods for classification and analysis of multivariate
observations. Paper presented at the Proceedings of the fifth Berkeley symposium on
mathematical statistics and probability.
McQuitty, L. L. (1957). Elementary linkage analysis for isolating orthogonal and oblique
types and typal relevancies. Educational and Psychological Measurement, 17(2),
207-229.
Mirkin, B. (2001). Reinterpreting the category utility function. Machine Learning, 45(2),
219-228.
Naldi, M. C., Carvalho, A. C., & Campello, R. J. (2013). Cluster ensemble selection
based on relative validity indexes. Data mining and knowledge discovery, 1-31.
Nayak, J., Naik, B., & Behera, H. (2015). Fuzzy C-means (FCM) clustering algorithm: a
decade review from 2000 to 2014 Computational Intelligence in Data Mining-
Volume 2 (pp. 133-149): Springer.
Parvin, H., Minaei-Bidgoli, B., Alinejad-Rokny, H., & Punch, W. F. (2013). Data
weighing mechanisms for clustering ensembles. Computers & Electrical
Engineering, 39(5), 1433-1450.
Punera, K., & Ghosh, J. (2008). Consensus-based ensembles of soft clusterings. Applied
Artificial Intelligence, 22(7-8), 780-810.
Rashedi, E., & Mirzaei, A. (2011). A novel multi-clustering method for hierarchical
clusterings based on boosting Electrical Engineering (ICEE), 2011 19th Iranian
Conference on (pp. 1-4): IEEE.
Rashedi, E., & Mirzaei, A. (2013). A hierarchical clusterer ensemble method based on
boosting theory. Knowledge-Based Systems, 45, 83-93.
Ren, Y., Domeniconi, C., Zhang, G., & Yu, G. (2016). Weighted-object ensemble
clustering: methods and analysis. Knowledge and Information Systems, 1-29.
Sadeghian, A. H., & Nezamabadi-pour, H. (2014). Gravitational ensemble clustering
Intelligent Systems (ICIS), 2014 Iranian Conference on (pp. 1-6): IEEE.
Saeed, F., Ahmed, A., Shamsir, M. S., & Salim, N. (2014). Weighted voting-based
consensus clustering for chemical structure databases. Journal of computer-aided
molecular design, 28(6), 675-684.
Sander, J., Ester, M., Kriegel, H.-P., & Xu, X. (1998). Density-based clustering in spatial
databases: The algorithm gdbscan and its applications. Data mining and knowledge
discovery, 2(2), 169-194.
Shi, J., & Malik, J. (2000). Normalized cuts and image segmentation. IEEE Transactions
on pattern analysis and machine intelligence, 22(8), 888-905.