On-line Evolutionary Exponential Family Mixture
Proceedings of the Twenty-First International Joint Conference on Artificial Intelligence. Pages 1610-1614.
Abstract
This paper deals with evolutionary clustering, which refers to the problem of clustering data with distribution drifting along time. Starting from a density estimation view to clustering problems, we propose two general on-line frameworks. In the first framework, i.e.,historical data dependent (HDD), current model distribution is designed to approximate both current and historical data distributions. In the second framework, i.e., historical model dependent (HMD), current model distribution is designed to approximate both current data distribution and historical model distribution. Both frameworks are based on the general exponential family mixture (EFM) model. As a result, all conventional clustering algorithms based on EFMs can be extended to evolutionary setting under the two frameworks. Empirical results validate the two frameworks.Jianwen Zhang, Yangqiu Song, Gang Chen, Changshui Zhang
@inproceedings{ijcai2009p269,
title = {On-line Evolutionary Exponential Family Mixture},
author = {Zhang, Jianwen and Song, Yangqiu and Chen, Gang and Zhang, Changshui},
booktitle = {Proceedings of the Twenty-First International Joint Conference on
Artificial Intelligence, {IJCAI-09}},
publisher = {International Joint Conferences on Artificial Intelligence},
editor = {Hiroaki Kitano},
pages = {1610--1614},
year = {2009},
url = {https://ijcai.org/proceedings/2009/269},
}
TY - CPAPER TI - On-line Evolutionary Exponential Family Mixture AU - Zhang, Jianwen AU - Song, Yangqiu AU - Chen, Gang AU - Zhang, Changshui T2 - Proceedings of the Twenty-First International Joint Conference on Artificial Intelligence, IJCAI-09 PB - International Joint Conferences on Artificial Intelligence PY - 2009 SP - 1610 EP - 1614 UR - https://ijcai.org/proceedings/2009/269 ER -Copied
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