Clustering Nathaniel Lewis How it works Read in Historic Data - PowerPoint PPT Presentation

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Clustering Nathaniel Lewis How it works Read in Historic Data Generate Centroids randomly Assign data points to Centroids Average values of data points and adjust Centroids Repeat 3 & 4 until no data points are reassigned Read in new

Clustering Nathaniel Lewis
How it works Read in Historic Data Generate Centroids randomly Assign data points to Centroids Average values of data points and adjust Centroids Repeat 3 & 4 until no data points are reassigned Read in new data and predict outcome based on closest centroid
K-Modes Derivative of k-means Works with Nominal data Uses number of different answers to determine distance Centroid values are adjusted to the mode of data points assigned to it
Issues I had <Template ItemType> Object Linking Segmentation Faults
Summary K-Means finds clusters in numeric data K-Modes finds clusters in nominal data Clusters are used in predictions Programming is hard.
Citations Coates, A., & Ng, A. Y. (1970, January 01). Learning Feature Representations with K-Means. Retrieved November 26, 2017, from https://link.springer.com/chapter/10.1007/978-3-642-35289-8_30 Honarkhah, M; Caers, J (2010). "Stochastic Simulation of Patterns Using Distance-Based Pattern Modeling". Mathematical Geosciences . 42 (5): 487 – 517. doi:10.1007/s11004-010-9276-7 K-modes. (2014, September 14). Retrieved November 26, 2017, from https://shapeofdata.wordpress.com/2014/03/04/k-modes/ Lloyd, S. P. (1957). "Least square quantization in PCM". Bell Telephone Laboratories Paper . Published in journal much later: Lloyd., S. P. (1982). "Least squares quantization in PCM" (PDF). IEEE Transactions on Information Theory . 28 (2): 129 – 137. doi:10.1109/TIT.1982.1056489. Retrieved 2009-04-15.
Citations 2 MacQueen, J. B. (1967). Some Methods for classification and Analysis of Multivariate Observations . Proceedings of 5th Berkeley Symposium on Mathematical Statistics and Probability. 1 . University of California Press. pp. 281 – 297. MR 0214227. Zbl 0214.46201. Retrieved 2009-04-07. Steinhaus, H. (1957). "Sur la division des corps matériels en parties". Bull. Acad. Polon. Sci. (in French). 4 (12): 801 – 804. MR 0090073. Zbl 0079.16403. Wagstaff, K., Cardie, C., Rogers, S., & Schroedl, S. (n.d.). Constrained K-means Clustering with Background Knowledge (2001 ed., Proceedings of the Eighteenth International Conference on Machine Learning, pp. 577-584, Rep.).

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