Quantum Clustering Algorithm based on Exponent Measuring Distance
Yao Zhang, Peng Wang, Chen Gao-yun, Chen Dong-Dong, Rui Feng Ding, Yan Zhang · 2008
The principle advantage and shortcoming of quantum clustering algorithm (QC) is analyzed. Based on its shortcomings, an improved algorithm - exponent distance-based quantum clustering algorithm (EQDC) is produced. It improved the iterative procedure of QC algorithm and used exponent distance formula to measure the distance between data points and the cluster centers. Experimental results demonstrate that the cluster accuracy of EDQC outperforms that of QC, and the exponent distance formula used in the clustering process performs better than the Euclidean distance in data preprocessing. What's more, the IRIS dataset can come to a satisfied result without preprocessing.