@Article{cmc.2020.011399,
AUTHOR = {Gongde Guo, Kai Yu, Hui Wang, Song Lin, Yongzhen Xu, Xiaofeng Chen},
TITLE = {Quantum Hierarchical Agglomerative Clustering Based on One  Dimension Discrete Quantum Walk with Single-Point Phase  Defects},
JOURNAL = {Computers, Materials \& Continua},
VOLUME = {65},
YEAR = {2020},
NUMBER = {2},
PAGES = {1397--1409},
URL = {http://www.techscience.com/cmc/v65n2/39883},
ISSN = {1546-2226},
ABSTRACT = {As an important branch of machine learning, clustering analysis is widely used 
in some fields, e.g., image pattern recognition, social network analysis, information
security, and so on. In this paper, we consider the designing of clustering algorithm in 
quantum scenario, and propose a quantum hierarchical agglomerative clustering 
algorithm, which is based on one dimension discrete quantum walk with single-point 
phase defects. In the proposed algorithm, two nonclassical characters of this kind of 
quantum walk, localization and ballistic effects, are exploited. At first, each data point is 
viewed as a particle and performed this kind of quantum walk with a parameter, which is 
determined by its neighbors. After that, the particles are measured in a calculation basis. 
In terms of the measurement result, every attribute value of the corresponding data point
is modified appropriately. In this way, each data point interacts with its neighbors and 
moves toward a certain center point. At last, this process is repeated several times until 
similar data points cluster together and form distinct classes. Simulation experiments on 
the synthetic and real world data demonstrate the effectiveness of the presented algorithm. 
Compared with some classical algorithms, the proposed algorithm achieves better 
clustering results. Moreover, combining quantum cluster assignment method, the 
presented algorithm can speed up the calculating velocity.},
DOI = {10.32604/cmc.2020.011399}
}