Efficient K-means with Region Segment and Outlier Detection

NAVIGATE

Table of Contents

STATISTICS

Viewed334

Downloads577

Efficient K-means with Region Segment and Outlier Detection

[HTML] PDF下载 (577)

[1]YIN Hongwei,HANG Yuqing,HU Wenjun.Efficient K-means with Region Segment and Outlier Detection[J].Journal of Zhengzhou University (Engineering Science),2024,45(03):80-88.[doi:10. 13705/ j. issn. 1671-6833. 2024. 03. 010]

Copy

Journal of Zhengzhou University (Engineering Science)[ISSN 1671-6833/CN 41-1339/T] Volume: 45 Number of periods: 2024 03 Page number: 80-88 Column: Public date: 2024-04-20

Title:: Efficient K-means with Region Segment and Outlier Detection

Author(s):: YIN Hongwei¹; 2; HANG Yuqing¹; 2; HU Wenjun¹; 2; 1. College of Information Engineering, Huzhou University, Huzhou 313000, China; 2. Zhejiang Key Laboratory of Smart Management & Application of Modern Agricultural Resources, Huzhou University, Huzhou 313000, China

Keywords:: clustering; K-means; outlier detection; region segment; near neighbor clusters search; adaption

CLC:: TP391

DOI:: 10. 13705/ j. issn. 1671-6833. 2024. 03. 010

Abstract:: In the traditional K-means and many improved algorithms, the inability to explicitly handle outliers, resulted in their poor clustering performance. To solve this problem, in this paper, an efficient K-means with region segment and outlier detection was proposed. Firstly, to obtain better clustering results, an unified clustering model to form an interactive collaboration between outlier detection and clustering was constructed. Secondly, to improve algorithm efficiency, clusters were adaptively segmented through near neighbor clusters search to reduce redundant calculations. Finally, on synthetic datasets and real datasets were tested to verify the effectiveness of the proposed method. The experimental results showed that EK-means algorithm outperformed other algorithms in terms of clustering performance and execution efficiency. The ACC could reach 0. 911 in the Wine dataset.

References:: [1] NIE F P, LI Z H, WANG R, et al. An effective and efficient algorithm for K-means clustering with new formulation[J]. IEEE Transactions on Knowledge and Data Engineering, 2023, 35(4): 3433-3443.
[2] BAI L, LIANG J Y, ZHAO Y X. Self-constrained spectral clustering[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2023, 45(4): 5126-5138.
[3] ZHANG Q H, DAI Y Y, WANG G Y. Density peaks clustering based on balance density and connectivity[J]. Pattern Recognition, 2023, 134: 109052.
[4] HIRECHE C, DRIAS H, MOULAI H. Grid based clustering for satisfiability solving [ J]. Applied Soft Computing,2020, 88: 106069.
[5] MONATH N, KOBREN A, KRISHNAMURTHY A, et al. Scalable hierarchical clustering with tree grafting[C]∥Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining. New York:ACM, 2019: 1438-1448.
[6] WAN Y C, LIU X B, WU Y, et al. ICGT: a novel incremental clustering approach based on GMM tree[ J].Data & Knowledge Engineering, 2018, 117: 71-86.
[7] 鲁斌, 范晓明. 基于改进自适应k 均值聚类的三维点云骨架提取的研究[J]. 自动化学报, 2022, 48(8):1994-2006.LU B, FAN X M. Research on 3D point cloud skeleton extraction based on improved adaptive k-means clustering[ J]. Acta Automatica Sinica, 2022, 48 ( 8): 1994-2006.
[8] LI Z, TANG C, ZHENG X, et al. Unified K-means coupledself-representation and neighborhood kernel learning for clustering single-cell RNA-sequencing data[J]. Neurocomputing, 2022, 501: 715-726.
[9] IM S, QAEM M M, MOSELEY B, et al. Fast noise removal for K-means clustering[C]∥ the 23rd International Conference on Artificial Intelligence and Statistics(AISTATS) 2020. Palermo: AISTATS, 2020: 456-466.[10] LI Y M, ZHANG Y, TANG Q T, et al. T-k-means: a robust and stable k-means variant[C]∥International Conferenceon Acoustics, Speech and Signal Processing (ICASSP). Piscataway: IEEE, 2021: 3120-3124.
[11] GRUNAU C, ROZHON V. Adapting K-means algorithms for outliers[C] ∥the 39th International Conference on Machine Learning. Baltimore: ICML, 2022: 7845-7886.
[12] ZHANG Z, FENG Q L, HUANG J Y, et al. A local search algorithm for k-means with outliers[J]. Neurocomputing,2021, 450: 230-241.
[13] HUANG S D, REN Y Z, XU Z L. Robust multi-view data clustering with multi-view capped-norm K-means[ J]. Neurocomputing, 2018, 311: 197-208.
[14] HUANG S D, KANG Z, XU Z L, et al. Robust deep kmeans: an effective and simple method for data clustering[J]. Pattern Recognition, 2021, 117: 107996.
[15] HAUTAMÄKI V, CHEREDNICHENKO S, KÄRKKÄINENI, et al. Improving k-means by outlier removal[C]∥Proceedings of the 14th Scandinavian conference on Image Analysis.New York: ACM, 2005: 978-987.
[16] PENG D W, CHEN Z Z, FU J C, et al. Fast k-means clustering based on the neighbor information[C]∥ISEEIE 2021: 2021 International Symposium on Electrical, Electronics and Information Engineering. New York: ACM,2021: 551-555.
[17] GIFFON L, EMIYA V, KADRI H, et al. Quick-means: accelerating inference for K-means by learning fast transforms[J]. Machine Learning, 2021, 110(5): 881-905.
[18] HAMERLY G. Making k-means even faster [ J]. Proceedings of the 10th SIAM International Conference on Data Mining, 2010: 130-140.
[19] DRAKE J. Faster K-means clustering[EB/ OL]. (2013-09 - 24) [ 2023 - 06 - 13]. http: ∥hdl. handle. net/2104/ 8826.
[20] NEWLING J, FLEURET F. Fast K-means with accurate bounds[C]∥Proceedings of the 33rd International Conferenceon International Conference on Machine Learning-Volume 48. New York:ACM, 2016: 936-944.
[21] XIA S Y, PENG D W, MENG D Y, et al. Ball $ k $ kmeans: fast adaptive clustering with No bounds [ J].IEEE Transactions on Pattern Analysis and Machine Intelligence, 2022, 44(1): 87-99.
[22] ARTHUR D, VASSILVITSKII S. K-means + +: the advantages of careful seeding[C]∥Proceedings of the eighteenth annual ACM-SIAM symposium on Discrete algorithms.New York: ACM, 2007: 1027-1035.
[23] KAUFMAN L, ROUSSEEUW P. Clustering by means of medoids[EB/ OL]. (1987-01-01) [2023-06-13]. https:∥ www. researchgate. net/ publication/ 243777819 _Clustering_by_Means_of_Medoids.

Similar References:

Memo

Last Update: 2024-04-29