Cited By
View all- Sharma PKurban HDalkilic M(2022)DCEM: An R package for clustering big data via data-centric modification of Expectation MaximizationSoftwareX10.1016/j.softx.2021.10094417(100944)Online publication date: Jan-2022
Case Study: Clustering Big Stellar Data with EM*
Pages 271 - 272
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 $\sim$ 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.
References
[1]
Mark Jenne, Owen Boberg, Hasan Kurban, and Mehmet Dalkilic. 2014. Studying the milky way galaxy using paraheap-k. Computer, Vol. 47, 9 (2014), 26--33.
[2]
Hasan Kurban, Mark Jenne, and Mehmet M Dalkilic. 2016. EM*: An EM Algorithm for Big Data. In Data Science and Advanced Analytics (DSAA), 2016 IEEE International Conference on. IEEE, 312--320.
[3]
Hasan Kurban, Mark Jenne, and Mehmet M Dalkilic. 2017. Using data to build a better EM: EM* for big data. International Journal of Data Science and Analytics (2017), 1--15.
Index Terms
- Case Study: Clustering Big Stellar Data with EM*
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Published In
December 2017
288 pages
ISBN:9781450355490
DOI:10.1145/3148055
- General Chairs:
- Ashiq Anjum,
- Alan Sill,
- Program Chairs:
- Xinghui Zhao,
- Mohsen Farid,
- Shrideep Pallickara,
- Jiannong Cao
Copyright � 2017 Owner/Author.
Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for third-party components of this work must be honored. For all other uses, contact the Owner/Author.
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- SIGARCH: ACM Special Interest Group on Computer Architecture
- IEEE TCSC: IEEE Technical Committee on Scalable Computing
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Association for Computing Machinery
New York, NY, United States
Publication History
Published: 05 December 2017
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UCC '17
Sponsor:
- SIGARCH
- IEEE TCSC
UCC '17: 10th International Conference on Utility and Cloud Computing
December 5 - 8, 2017
Texas, Austin, USA
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BDCAT '17 Paper Acceptance Rate 27 of 93 submissions, 29%;
Overall Acceptance Rate 27 of 93 submissions, 29%
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Cited By
View all- Sharma PKurban HDalkilic M(2022)DCEM: An R package for clustering big data via data-centric modification of Expectation MaximizationSoftwareX10.1016/j.softx.2021.10094417(100944)Online publication date: Jan-2022
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