@inproceedings{3797253bd31f4ba1b0fb5a4094ddfd73,
title = "Spectral clustering algorithm based on attribute weight of information entropy",
abstract = "For the spectral clustering algorithm in the large-scale samples, there are bottlenecks in the storage space and computing time, and the paper analyzes the current common solutions, namely based on the sparse t-nearest neighbor spectral clustering. In order to improve the accuracy of the spectral clustering algorithms, the Euclidean distance based on the attribute weight of information entropy is proposed to calculate the similarity between the samples. First, calculate the weight of each attribute of the sample and then calculate the similarity between the samples. The degree matrix is applied to the spectral clustering of sparse t-nearest neighbors in the last number of numbers. The experimental results show that the clustering accuracy of the method on some data sets is higher than that of the original spectral clustering algorithm.",
keywords = "Information entropy, Sparse t-nearest neighbor, Spectral clustering, Weighted Euclidean distance",
author = "Guohong Liang and Ying Li",
note = "Publisher Copyright: {\textcopyright} Springer Nature Singapore Pte Ltd. 2019.; International Conference on Intelligent Computing, Communication and Devices, ICCD 2017 ; Conference date: 09-12-2017 Through 10-12-2017",
year = "2019",
doi = "10.1007/978-981-10-8944-2_17",
language = "英语",
isbn = "9789811089435",
series = "Advances in Intelligent Systems and Computing",
publisher = "Springer Verlag",
pages = "137--142",
editor = "Srikanta Patnaik and Vipul Jain",
booktitle = "Recent Developments in Intelligent Computing, Communication and Devices - Proceedings of ICCD 2017",
}