@inproceedings{03c9d700f49d4430ab9a2c0aa07c51bb,
title = "A novel approach to optimization of iterative machine learning algorithms: Over heap structure",
abstract = "Iterative machine learning algorithms, i.e., k-means (KM), expectation maximization (EM), become overwhelmed with big data since all data points are being continually and indiscriminately visited while a cost is being minimized. In this work, we demonstrate (1) an optimization approach to reduce training run-time complexity of iterative machine learning algorithms and (2) implementation of this framework over KM algorithm. We call this extended KM algorithm, KM∗. The experimental results show that KM∗ outperforms KM over big real world and synthetic data sets. Lastly, we demonstrate the theoretical elements of our work.",
keywords = "big data, clustering, data mining, heap, k-means, machine learning, optimization",
author = "Hasan Kurban and Dalkilic, {Mehmet M.}",
note = "Publisher Copyright: {\textcopyright} 2017 IEEE.; 5th IEEE International Conference on Big Data, Big Data 2017 ; Conference date: 11-12-2017 Through 14-12-2017",
year = "2017",
month = jul,
day = "1",
doi = "10.1109/BigData.2017.8257917",
language = "English",
series = "Proceedings - 2017 IEEE International Conference on Big Data, Big Data 2017",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "102--109",
editor = "Jian-Yun Nie and Zoran Obradovic and Toyotaro Suzumura and Rumi Ghosh and Raghunath Nambiar and Chonggang Wang and Hui Zang and Ricardo Baeza-Yates and Ricardo Baeza-Yates and Xiaohua Hu and Jeremy Kepner and Alfredo Cuzzocrea and Jian Tang and Masashi Toyoda",
booktitle = "Proceedings - 2017 IEEE International Conference on Big Data, Big Data 2017",
address = "United States",
}