@inproceedings{1ef4471b484f44d6ad0b495e3a04aa03,
title = "Quantum clustering algorithm based on exponent measuring distance",
abstract = "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 distanceformula 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.",
keywords = "Clustering accuracy, Data preprocessing, Exponent distance-based quantum clustering algorithm (EDQC algorithm), Measuring formula, Quantum clustering algorithm, Quantum potential",
author = "Yao Zhang and Peng Wang and Chen, \{Gao Yun\} and Chen, \{Dong Dong\} and Rui Ding and Yan Zhang",
year = "2008",
doi = "10.1109/KAMW.2008.4810518",
language = "English",
isbn = "9781424435296",
series = "2008 IEEE International Symposium on Knowledge Acquisition and Modeling Workshop Proceedings, KAM 2008",
pages = "436--439",
booktitle = "2008 IEEE International Symposium on Knowledge Acquisition and Modeling Workshop Proceedings, KAM 2008",
note = "2008 IEEE International Symposium on Knowledge Acquisition and Modeling Workshop, KAM 2008 ; Conference date: 21-12-2008 Through 22-12-2008",
}