Pseudo k-means approach to the classifying problem
Chin‐Wang Tao, Wiley E. Thompson, Ramón Parra-Loera · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · 1992
This paper presents a methodology for the classifying problem based upon a pseudo k-means algorithm. Both supervised and unsupervised classifying algorithms are presented here to show the flexibility of the pseudo k-means algorithm. The supervised algorithm is computationally efficient compared with the k-nn algorithm. The unsupervised algorithm avoids the error and time consuming problem due to the improper selection of initial class centers in the k-means algorithm. The pseudo k-means algorithm is easy to extend to the high dimension situation. Examples are presented to illustrate the effectiveness of the approach.