An Improved Automated Spectral Clustering Algorithm
1
조회
0
다운로드
0
인용
0
참고문헌
키워드
초록
In this paper, an improved automated spectral clustering (IASC) algorithm is proposed to address the limitationsof the traditional spectral clustering (TSC) algorithm, particularly its inability to automatically determine thenumber of clusters. Firstly, a cluster number evaluation factor based on the optimal clustering principle isproposed. By iterating through different k values, the value corresponding to the largest evaluation factor wasselected as the first-rank number of clusters. Secondly, the IASC algorithm adopts a density-sensitive distanceto measure the similarity between the sample points. This rendered a high similarity to the data distributed inthe same high-density area. Thirdly, to improve clustering accuracy, the IASC algorithm uses the cosine angleclassification method instead of K-means to classify the eigenvectors. Six algorithms—K-means, fuzzy Cmeans,TSC, EIGENGAP, DBSCAN, and density peak—were compared with the proposed algorithm on sixdatasets. The results show that the IASC algorithm not only automatically determines the number of clustersbut also obtains better clustering accuracy on both synthetic and UCI datasets.
Abstract
In this paper, an improved automated spectral clustering (IASC) algorithm is proposed to address the limitationsof the traditional spectral clustering (TSC) algorithm, particularly its inability to automatically determine thenumber of clusters. Firstly, a cluster number evaluation factor based on the optimal clustering principle isproposed. By iterating through different k values, the value corresponding to the largest evaluation factor wasselected as the first-rank number of clusters. Secondly, the IASC algorithm adopts a density-sensitive distanceto measure the similarity between the sample points. This rendered a high similarity to the data distributed inthe same high-density area. Thirdly, to improve clustering accuracy, the IASC algorithm uses the cosine angleclassification method instead of K-means to classify the eigenvectors. Six algorithms—K-means, fuzzy Cmeans,TSC, EIGENGAP, DBSCAN, and density peak—were compared with the proposed algorithm on sixdatasets. The results show that the IASC algorithm not only automatically determines the number of clustersbut also obtains better clustering accuracy on both synthetic and UCI datasets.
참고문헌 (28)
- L. Bai, "Survey of spectral clustering algorithms", Computer Engineering and Applications, 57(14), pp.15~26 (2021)[DOI]
- Z. Xia, "A privacy-preserving and copy-deterrence content-based image retrieval scheme in cloud computing", IEEE Transactions on Information Forensics and Security, 11(11), pp.2594~2608 (2016)[DOI]
- K. Xia, "Oriented grouping-constrained spectral clustering for medical imaging segmentation", Multimedia Systems, 26, pp.27~36 (2020)[DOI]
- Z. Yu, "Adaptive fuzzy consensus clustering framework for clustering analysis of cancer data", IEEE, 2015, pp.12(4), 887~901
- X. Jiang, "Label propagation-based semi-supervised feature selection on decoding clinical phenotypes with RNA-seq data", BMC Medical Genomics, 14(Suppl 1) (2021)[DOI]
- U. Agrawal, "Combining clustering and classification ensembles : A novel pipeline to identify breast cancer profiles", Artificial Intelligence in Medicine, 97, pp.27~37 (2019)
- D. Xu, "A Novel low rank spectral clustering method for face identification", Recent Patents on Engineering, 13(4), pp.387~394 (2019)[DOI]
- S. Wazarkar, "A survey on image data analysis through clustering techniques for real world applications", Journal of Visual Communication and Image Representation, 55, pp.596~626 (2018)[DOI]
- Z. Ding, "Structural damage identification with uncertain modelling error and measurement noise by clustering based tree seeds algorithm", Engineering Structures, 185, pp.301~314 (2019)[DOI]
- Q. Wu, "Research and implementation of Chinese text clustering algorithm", Ph. D., pp.2010
- M. Ester, "A density-based algorithm for discovering clusters in large spatial databases with noise", Proceedings of the Second International Conference on Knowledge Discovery and Data Mining (KDD), pp.226~231 (1996)
- A. Rodriguez, "Clustering by fast search and find of density peaks", Science, 344(6191), pp.1492~1496 (2014)[DOI]
- A. Ng, "On spectral clustering : analysis and an algorithm", Advances in Neural Information Processing Systems, 14, pp.849~856 (2001)
- M. Fiedler, "Algebraic connectivity of graphs", Czechoslovak Mathematical Journal, 23(2), pp.298~305 (1973)[DOI]
- J. Shi, "Normalized cuts and image segmentation", IEEE Transactions on Pattern Analysis and Machine Intelligence, 22(8), pp.888~905 (2000)[DOI]
- H. Liu, "Transformer fault warning based on spectral clustering and decision tree", Electronics, 12(2) (2023)[DOI]
- L. Hagen, "New spectral methods for ratio cut partitioning and clustering", IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems, 11(9), pp.1074~1085 (1992)[DOI]
- W. Z. Kong, "Automatic spectral clustering based on eigengap and orthogonal eigenvector", Acta Electronica Sinica, 38(8), pp.1880~1885 (2010)
- Z. Hu, "Adaptive spectral clustering algorithm based on artificial bee colony algorithm", Journal of Chongqing University of Technology(Natural Science Edition), 34(3), pp.137~144 (2020)[DOI]
- R. Porter, "A robust automatic clustering scheme for image segmentation using wavelets", IEEE Transactions on Image Processing, 5(4), pp.662~665 (1996)[DOI]
- C. Gao, "An automatic technique to determine cluster number for complex biologic datasets", China Journal of Bioinformatics, 8(4), pp.295~298 (2010)[DOI]
- H. Chen, "Fuzzy clustering algorithm for automatic identification of clusters", Acta Electronica Sinica, 45(3), pp.687~694 (2017)
- L. Wang, "Density-sensitive spectral clustering", Acta Electronica Sinica, 35(8), pp.1577~1581 (2007)
- O. Chapelle, "Semi-supervised classification by low density separation", Proceedings of the 10th International Workshop on Artificial Intelligence and Statistics, pp.57~64 (2005)
- P. Yang, "Spectral clustering with density sensitive similarity function", Knowledge-Based Systems, 24(5), pp.621~628 (2011)[DOI]
- R. W. Floyd, "Algorithm 97 : shortest path", Communications of the ACM, 5(6), pp.345~345 (1962)[DOI]
- "Machine learning datasets" (2023)[URL]
- X. Xu, "A robust density peaks clustering algorithm with density-sensitive similarity", Knowledge-Based Systems, 200 (2020)[DOI]