Abstract
Hybrid Gene Trajectory Clustering (GTC) algorithm [1,2] proves to be a good candidate to cluster multi-dimensional noisy time series. In this paper we apply the hybrid GTC to learn the structure of the stock market and to infer interesting relationships out of closing prices data. We conclude that hybrid GTC can successfully identify homogeneous and stable stock clusters and these clusters can further help the investors.
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Moldovan, D., Silaghi, G.C. (2009). Gene Trajectory Clustering for Learning the Stock Market Sectors. In: Kolehmainen, M., Toivanen, P., Beliczynski, B. (eds) Adaptive and Natural Computing Algorithms. ICANNGA 2009. Lecture Notes in Computer Science, vol 5495. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-04921-7_57
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DOI: https://doi.org/10.1007/978-3-642-04921-7_57
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-04920-0
Online ISBN: 978-3-642-04921-7
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