Weighted mahalanobis distance-based quantum clustering approach for heterogeneous data
Shitong Wang · Computer Engineering and Applications Journal · 2009
The dissimilarity measure and clustering approach about the heterogeneous dataset are studied,and a Weighted Mahalanobis Distance-based Quantum Clustering(WMDQC) algorithm is presented in this paper.Data often do appear in homogeneous groups,the WMDQC utilizes the structural information to improve the clustering accuracy.Unlike the numeric data, categorical data are often unbalancedly distributed,whose distribution are often unrelated with their distance measure.These characteristics are very similar to the particle world in quantum mechanism,so the WMDQC ascertains the clustering centers by the rewriting quantum potential.Further,a WMDQC-based method WMDQCM is proposed,the WMDQCM mines the structural clue by the agglomerative hierarchical clustering AHC algorithm to construct the weight matrix.By presenting the above to the WMDQC,the final clustering results are obtained.The new WMDQCM exhibits its robustness to initialization and clustering capability to heterogeneous dataset.Experimental results compared with other methods demonstrate that the proposed method has promising performance.