Quantum Density Peak Clustering Algorithm

A widely used clustering algorithm, density peak clustering (DPC), assigns different attribute values to data points through the distance between data points, and then determines the number and range of clustering by attribute values. However, DPC is inefficient when dealing with scenes with a large...

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Bibliographic Details
Published in:Entropy (Basel, Switzerland) Vol. 24; no. 2; p. 237
Main Authors: Wu, Zhihao, Song, Tingting, Zhang, Yanbing
Format: Journal Article
Language:English
Published: Switzerland MDPI AG 03.02.2022
MDPI
Subjects:
Algorithms
Circuits
Cluster analysis
Clustering
Communication
Complexity
Data points
Density
Principal components analysis
quantum algorithm
quantum computation
quantum information
Values
quantum algorithm
quantum information
quantum computation
ISSN:1099-4300, 1099-4300
Online Access:Get full text
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Summary:A widely used clustering algorithm, density peak clustering (DPC), assigns different attribute values to data points through the distance between data points, and then determines the number and range of clustering by attribute values. However, DPC is inefficient when dealing with scenes with a large amount of data, and the range of parameters is not easy to determine. To fix these problems, we propose a quantum DPC (QDPC) algorithm based on a quantum DistCalc circuit and a Grover circuit. The time complexity is reduced to O(log(N2)+6N+N), whereas that of the traditional algorithm is O(N2). The space complexity is also decreased from O(N·⌈logN⌉) to O(⌈logN⌉).
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ISSN:1099-4300
1099-4300
DOI:10.3390/e24020237