STOC '01 Proceedings of the thirty-third annual ACM symposium on Theory of computing
STOC '01 Proceedings of the thirty-third annual ACM symposium on Theory of computing
Quantum computation and quantum information
Quantum computation and quantum information
ICALP '98 Proceedings of the 25th International Colloquium on Automata, Languages and Programming
Quantum Information Processing
Exponential algorithmic speedup by a quantum walk
Proceedings of the thirty-fifth annual ACM symposium on Theory of computing
Language model-based document clustering using random walks
HLT-NAACL '06 Proceedings of the main conference on Human Language Technology Conference of the North American Chapter of the Association of Computational Linguistics
Proceedings of the 24th international conference on Machine learning
Adaptive dimension reduction using discriminant analysis and K-means clustering
Proceedings of the 24th international conference on Machine learning
Algorithms for quantum computation: discrete logarithms and factoring
SFCS '94 Proceedings of the 35th Annual Symposium on Foundations of Computer Science
Quantum speed-up for unsupervised learning
Machine Learning
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The enormous successes have been made by quantum algorithms during the last decade. In this paper, we combine the quantum walk (QW) with the problem of data clustering, and develop two clustering algorithms based on the one-dimensional discrete-time QW. Then, the position probability distributions induced by QW in these algorithms are investigated, which also indicates the possibility of obtaining better results. Consequently, the experimental results have demonstrated that data points in datasets are clustered reasonably and efficiently, and the clustering algorithms have fast rates of convergence. Moreover, the comparison with other algorithms also provides an indication of the effectiveness of the proposed approach.