Efficient Data Clustering by Local Density Approximation

The clustering task is a key part of the data mining process. In today's context of massive data, methods with a computational complexity more than linear are unlikely to be applied practically. In this paper, we begin by a simple assumption: local projections of the data should allow to distinguish local cluster structures. From there, we describe how to obtain "pure" local sub-groupings of points, from projections on randomly chosen lines. The clustering of the data is obtained from the clustering of these sub-groupings. Our method has a linear complexity in the dataset size, and requires only one pass on the original dataset. Being local in essence, it can handle twisted geometries typical of many high-dimensional datasets. We describe the steps of our method and report encouraging results.

Authors

• Marc-Ismaël Akodjènou-Jeannin
• Patrick Gallinari

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