Hasan Kurban
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.
- 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. Google Scholar
Digital Library
- 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.Google ScholarCross Ref
- 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.Google Scholar
Index Terms
- Case Study: Clustering Big Stellar Data with EM*
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