Title :
An investigation into unsupervised clustering techniques
Author :
Lee, H.S. ; Younan, N.H.
Author_Institution :
Dept. of Electr. & Comput. Eng., Mississippi State Univ., MS, USA
Abstract :
The performance of several unsupervised clustering techniques is compared using two clearly separated 3-D data sets that are not separable by any hyperplane. The result shows that the self-organizing feature map can cluster data sets successfully without any prior information of given data while the k-means and the fuzzy k-means algorithm fail to cluster correctly
Keywords :
data analysis; data compression; fuzzy systems; matrix algebra; pattern recognition; self-organising feature maps; unsupervised learning; U-matrix method; data compression; exploratory data analysis; fuzzy k-means algorithm; k-means algorithm; self-organizing feature map; separated 3D data sets; simulation; unsupervised clustering techniques; Clustering algorithms; Data analysis; Fuzzy sets; Iterative algorithms; Nearest neighbor searches; Neural networks; Organizing; Partitioning algorithms; Pattern recognition; Signal processing algorithms;
Conference_Titel :
Southeastcon 2000. Proceedings of the IEEE
Conference_Location :
Nashville, TN
Print_ISBN :
0-7803-6312-4
DOI :
10.1109/SECON.2000.845446
