Fast k-Means Based on k-NN Graph

Abstract

In the era of big data, k-means clustering has been widely adopted as a basic processing tool in various contexts. However, its computational cost could be prohibitively high as the data size and the cluster number are large. It is well known that the processing bottleneck of k-means lies in the operation of seeking closest centroid in each iteration. In this paper, a novel solution towards the scalability issue of k-means is presented. In the proposal, k-means is supported by an approximate k-nearest neighbors graph. In the k-means iteration, each data sample is only compared to clusters that its nearest neighbors reside. Since the number of nearest neighbors we consider is much less than k, the processing cost in this step becomes minor and irrelevant to k. The processing bottleneck is therefore overcome. The most interesting thing is that k-nearest neighbor graph is constructed by iteratively calling the fast k-means itself. Comparing with existing fast k-means variants, the proposed algorithm achieves hundreds to thousands times speed-up while maintaining high clustering quality. As it is tested on 10 million 512-dimensional data, it takes only 5.2 hours to produce 1 million clusters. In contrast, to fulfill the same scale of clustering, it would take 3 years for traditional k-means.

GGL-Tox: Geometric Graph Learning for Toxicity Prediction

“…For example, nonlinear SVM can be capable of dealing with high-dimensional data but may not be robust to the presence of diverse chemical descriptors. 17 Deng and Zhao 18 reported that the computational cost of KNN increases exponentially with the size of the input samples. Recently, deep learning (DL) has attracted much attention for predicting the outcome of biological assays and becomes a key candidate for toxicity prediction due to its ability to bypass feature extraction.…”

Efficient Toxicity Prediction via Simple Features Using Shallow Neural Networks and Decision Trees

“…These models perform relatively better on smaller data sets with fewer preselected features. One key limitation of KNN algorithm is the exponential rise of computational cost with the size of the input samples. − In contrast, nonlinear SVMs can manage high dimensional data but do not exhibit sufficiently robust performance on diverse chemical descriptors …”