Data clustering using higher order statistics
Traditional k-means algorithms for data clustering are based on the assumption that the underlying distribution of the data is Gaussian. In this paper, we propose a new clustering algorithm that makes use of higher order statistics for improved data clustering when the distribution of the data is non-Gaussian. The algorithm uses as HOS-based decision measure which is derived from a series expansion of the multivariate probability density function in terms of the multivariate Gaussian and the Hermite polynomials. Experimental results are presented on the performance of the proposed algorithm.
IEEE Region 10 Annual International Conference, Proceedings/TENCON
Rajagopalan, A., Yeasin, M., & Chaudhuri, S. (1997). Data clustering using higher order statistics. IEEE Region 10 Annual International Conference, Proceedings/TENCON, 803-806. Retrieved from https://digitalcommons.memphis.edu/facpubs/13638