Unsupervised curve-based clustering
Yuhui Yao, Lihen Chen, Yan Qiu Chen · 2003
Clustering a data set that exhibits arbitrary shapes is an important research area in unsupervised data labeling. The paper is concerned with using a shape-matched curve as the prototype of that cluster. Data points are then labeled by those curve-represented clusters without any preknowledge and priori assumptions of hidden structures in the data set. Since the shapes of these curves are often dendritic, we call this learning process dendritic curve clustering (DCC). DCC makes use of fuzzy c-means, and each shape-matched cluster curve is achieved through cluster growing. Experimental results demonstrate the DCC approach is feasible.