K-nearest Neighbor Search by Random Projection Forests

K-nearest neighbor (kNN) search has wide applications in many areas, including data mining, machine learning, statistics and many applied domains. Inspired by the success of ensemble methods and the flexibility of tree-based methodology, we propose random projection forests (rpForests), for kNN search. rpForests finds kNNs by aggregating results from an ensemble of random projection trees with each constructed recursively through a series of carefully chosen random projections. rpForests achieves a remarkable accuracy in terms of fast decay in the missing rate of kNNs and that of discrepancy in the kNN distances. rpForests has a very low computational complexity. The ensemble nature of rpForests makes it easily run in parallel on multicore or clustered computers; the running time is expected to be nearly inversely proportional to the number of cores or machines. We give theoretical insights by showing the exponential decay of the probability that neighboring points would be separated by ensemble random projection trees when the ensemble size increases. Our theory can be used to refine the choice of random projections in the growth of trees, and experiments show that the effect is remarkable.Comment: 15 pages, 4 figures, 2018 IEEE Big Data Conferenc

Computer Information Systems and Industrial Management

This book constitutes the refereed proceedings of the 11th International Conference on Computer Information Systems and Industrial Management, CISIM 2012, held in Venice, Italy, in September 2012. The 35 revised full papers presented together with 2 keynote talks were carefully reviewed and selected from 80 submissions. The papers are organized in topical sections on security, access control and intrusion detection; pattern recognition and image processing; biometric applications; algorithms and data management; networking; and system models and risk assessment

Spectral Clustering Based on k-Nearest Neighbor Graph

Part 5: Algorithms and Data ManagementInternational audienceFinding clusters in data is a challenging task when the clusters differ widely in shapes, sizes, and densities. We present a novel spectral algorithm Speclus with a similarity measure based on modified mutual nearest neighbor graph. The resulting affinity matrix reflex the true structure of data. Its eigenvectors, that do not change their sign, are used for clustering data. The algorithm requires only one parameter – a number of nearest neighbors, which can be quite easily established. Its performance on both artificial and real data sets is competitive to other solutions