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A Novel Kernel Clustering Algorithm

July 26th, 2014

We have a new book chapter coming out now on high-dimensional data clustering in the book on partitional clustering algorithms. It is titled ‘Hubness-Based Clustering of High-Dimensional Data’ and it is an extension of our earlier work where we have shown that it is possible to exploit kNN hubs for effective data clustering in many dimensions.

In our chapter, we have extended the original algorithm to incorporate a ‘kernel trick’ in order to be able to handle non-hyperspherical clusters in the data. This has resulted in the Kernel Global Hubness-proportional K-Means algorithm (Kernel-GHPKM) that our experiments show as highly promising and preferable to standard kernel K-means on some high-dimensional datasets.

The implementation is available in Hub Miner and will be released very soon along with the rest of the library.

Stay tuned for more updates.

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