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P. S. Bradley and U. M. Fayyad, “Refining Initial Points for K-Means Clustering,” Proceedings of the 15th Conference on Machine Learning, Wisconsin, 24-27 July 1998, pp. 91-99.

has been cited by the following article:

• TITLE: A Time-Frequency Approach for Discrimination of Heart Murmurs

  AUTHORS: Sepideh Jabbari, Hassan Ghassemian

  KEYWORDS: Phonocardiogram (PCG), Murmur, Matching Pursuit (MP), Time-Frequency atom, Clustering

  JOURNAL NAME: Journal of Signal and Information Processing, Vol.2 No.3, August 12, 2011

  ABSTRACT: In this paper, a novel framework based on a time-frequency (TF) approach is proposed for detection of murmurs from heart sound signal. First, a high-resolution TF algorithm, matching pursuit, was used to decompose each heart beat into a series of TF atoms selected from a redundant dictionary. Next, representative components of murmurs were identified by clustering the selected atoms of all the beats into a finite number of clusters. Then, Wigner-Ville distribution of the representative components was used to generate a set of 8 features which were fed to a classifier. Experiments with a dataset consisting of heart sounds from 35 normal and 35 pathological subjects showed a classification accuracy of 95.71% in distinguishing murmurs from normal heart sounds.