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Generalized regression model for sequence matching and clustering

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Abstract

Linear relation has been found to be valuable in rule discovery of stocks, such as if stock X goes up a, stock Y will go down b. The traditional linear regression models the linear relation of two sequences faithfully. However, if a user requires clustering of stocks into groups where sequences have high linearity or similarity with each other, it is prohibitively expensive to compare sequences one by one. In this paper, we present generalized regression model (GRM) to match the linearity of multiple sequences at a time. GRM also gives strong heuristic support for graceful and efficient clustering. The experiments on the stocks in the NASDAQ market mined interesting clusters of stock trends efficiently.

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Authors and Affiliations

  1. Department of Computer Science and Computer Information Systems, University of Texas at Brownsville, Brownsville, TX, 78520, USA

    Hansheng Lei

  2. Govindaraju Computer Science and Engineering Department, The State University of New York at Buffalo, Amherst, NY, USA

    Venu Govindaraju

Authors
  1. Hansheng Lei
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  2. Venu Govindaraju
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Correspondence to Hansheng Lei.

Additional information

Hansheng Lei received his BE from Ocean University of China in 1998, MS from the University of Science and Technology of China in 2001, and Ph.D. from the University at Buffalo, the State University of New York in February 2006, all in computer science. He is currently an assistant professor in CS/CIS Department, University of Texas at Brownsville. His research interests include biometrics, pattern recognition, machine learning, and data mining.

Venu Govindaraju is a professor of Computer Science and Engineering at the University at Buffalo (UB), State University of New York. He received his B.-Tech. (Honors) from the Indian Institute of Technology (IIT), Kharagpur, India in 1986, and his Ph.D. degree in Computer Science from UB in 1992. His research is focused on pattern recognition applications in the areas of biometrics and digital libraries.

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Lei, H., Govindaraju, V. Generalized regression model for sequence matching and clustering. Knowl Inf Syst 12, 77–94 (2007). https://doi.org/10.1007/s10115-006-0008-8

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