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K-means*: Clustering by Gradual Data Transformation. Mikko Malinen and Pasi Fränti. Speech and Image Processing Unit School of Computing University of Eastern Finland. K-means* clustering. Gradual transformation of data. Fit the data to a model. Model. Intermediate. Final.
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K-means*: Clustering by Gradual Data Transformation Mikko Malinen and Pasi Fränti • Speech and Image Processing Unit • School of Computing • University of Eastern Finland
K-means* clustering Gradual transformation of data Fit the data to a model Model Intermediate Final Data
K-means clustering Iterate between two steps: 1. Assignment step Assign the points to the nearest centroids 2. Update step Update the location of centroids
Time Complexity Fixedk-means
s1 d = 2 n = 5000 k = 15 s2 d = 2 n = 5000 k = 15 s3 d = 2 n = 5000 k = 15 s4 d = 2 n = 5000 k = 15 bridge d = 16 n = 4096 k= 256 missa d = 16 n = 6480 k= 256 house d = 3 n=34000 k=256 thyroid d = 5 n = 215 k = 2 iris d = 4 n = 150 k = 2 wine d = 13 n = 178 k = 3 Datasets
Number of incorrect clusters All correct: proposed:36% k-means:14%
Number of incorrect clusters 1 incorrect: proposed:64% k-means:38%
Number of incorrect clusters 2 incorrect: proposed: 0% k-means:34%
Number of incorrect clusters 3 incorrect: proposed: 0% k-means:10%
Summary We have presented a clustering method based on gradual transformation of data and k-means. Instead of fitting the model to data, we fit the data to a model. The proposed method gives better mean square error than k-means.