Relational Algorithms for k-means Clustering
This addresses the challenge of efficient clustering for large-scale relational data, though it is incremental in adapting existing techniques to a new model.
The paper tackles the problem of k-means clustering directly on relational databases without converting to a matrix, developing an algorithm with running time potentially exponentially smaller than the number of data points and achieving an O(1)-approximate solution.
This paper gives a k-means approximation algorithm that is efficient in the relational algorithms model. This is an algorithm that operates directly on a relational database without performing a join to convert it to a matrix whose rows represent the data points. The running time is potentially exponentially smaller than $N$, the number of data points to be clustered that the relational database represents. Few relational algorithms are known and this paper offers techniques for designing relational algorithms as well as characterizing their limitations. We show that given two data points as cluster centers, if we cluster points according to their closest centers, it is NP-Hard to approximate the number of points in the clusters on a general relational input. This is trivial for conventional data inputs and this result exemplifies that standard algorithmic techniques may not be directly applied when designing an efficient relational algorithm. This paper then introduces a new method that leverages rejection sampling and the $k$-means++ algorithm to construct an O(1)-approximate k-means solution.