@inproceedings{0aafa2cf7f49489596d0add9c78301ac,
title = "Exponentially Consistent K-Means Clustering Algorithm Based on Kolmogrov-Smirnov Test",
abstract = "This paper studies clustering using a Kolmogorov-Smirnov based K-means algorithm. All data sequences are assumed to be generated by unknown continuous distributions. The pairwise KS distances of the distributions are assumed to be lower bounded by a certain positive constant. The convergence analysis of the proposed algorithms and upper bounds on the error probability are provided for both known and unknown number of clusters. More importantly, it is shown that the probability of error decays exponentially as the sample size of each data sequence goes to infinity, and the error exponent is only a function of the pairwise KS distances of the distributions. the analysis is validated by simulation results.",
keywords = "Clustering, Exponential consistency, K-means algorithm, Kolmogorov-Smirnov distance, Probability of error",
author = "Tiexing Wang and Bucci, {Donald J.} and Yingbin Liang and Biao Chen and Varshney, {Pramod K.}",
note = "Publisher Copyright: {\textcopyright} 2018 IEEE.; 2018 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2018 ; Conference date: 15-04-2018 Through 20-04-2018",
year = "2018",
month = sep,
day = "10",
doi = "10.1109/ICASSP.2018.8461730",
language = "English (US)",
isbn = "9781538646588",
series = "ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "2296--2300",
booktitle = "2018 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2018 - Proceedings",
}