Case study: Clustering big stellar data with EM*

Hasan Kurban, Can Kockan, Mark Jenne, Mehmet M. Dalkilic

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

1 Citation (Scopus)

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.

Original languageEnglish
Title of host publicationBDCAT 2017 - Proceedings of the 4th IEEE/ACM International Conference on Big Data Computing, Applications and Technologies
PublisherAssociation for Computing Machinery, Inc
Pages271-272
Number of pages2
ISBN (Electronic)9781450355490
DOIs
Publication statusPublished - 5 Dec 2017
Externally publishedYes
Event4th IEEE/ACM International Conference on Big Data Computing, Applications and Technologies, BDCAT 2017 - Austin, United States
Duration: 5 Dec 20178 Dec 2017

Publication series

NameBDCAT 2017 - Proceedings of the 4th IEEE/ACM International Conference on Big Data Computing, Applications and Technologies

Conference

Conference4th IEEE/ACM International Conference on Big Data Computing, Applications and Technologies, BDCAT 2017
Country/TerritoryUnited States
CityAustin
Period5/12/178/12/17

Keywords

  • Astronomy
  • Big data
  • Clustering
  • Expectation maximization
  • Heap

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