@inproceedings{85a56712476c45988d4f908425678b46,
title = "Case study: Clustering big stellar data with EM*",
abstract = "Without question, astronomy is about Big Data and clustering is a very common task over astronomy domain. The expectation-maximization algorithm is among the top 10 data mining algorithms used in scientific and industrial applications, however, we observe that astronomical community does not make use of it as a clustering algorithm. In this work, we cluster „ 1M stellar objects (simulated Galactic spectral data) via the traditional expectation-maximization algorithm for clustering (EM-T) and our extended EM-T algorithm that we call EM* and present the experimental results.",
keywords = "Astronomy, Big data, Clustering, Expectation maximization, Heap",
author = "Hasan Kurban and Can Kockan and Mark Jenne and Dalkilic, {Mehmet M.}",
note = "Publisher Copyright: {\textcopyright} 2017 Copyright held by the owner/author(s).; 4th IEEE/ACM International Conference on Big Data Computing, Applications and Technologies, BDCAT 2017 ; Conference date: 05-12-2017 Through 08-12-2017",
year = "2017",
month = dec,
day = "5",
doi = "10.1145/3148055.3149208",
language = "English",
series = "BDCAT 2017 - Proceedings of the 4th IEEE/ACM International Conference on Big Data Computing, Applications and Technologies",
publisher = "Association for Computing Machinery, Inc",
pages = "271--272",
booktitle = "BDCAT 2017 - Proceedings of the 4th IEEE/ACM International Conference on Big Data Computing, Applications and Technologies",
}