AGK: the Adaptive Grid K-means algorithm

An Zhou; Famao Mei; Zhenwei Gu; Hao Huang; Siyuan Dong

doi:10.1117/12.2641215

19 October 2022 AGK: the Adaptive Grid K-means algorithm

An Zhou, Famao Mei, Zhenwei Gu, Hao Huang, Siyuan Dong

Proceedings Volume 12294, 7th International Symposium on Advances in Electrical, Electronics, and Computer Engineering; 122943U (2022) https://doi.org/10.1117/12.2641215
Event: 7th International Symposium on Advances in Electrical, Electronics and Computer Engineering (ISAEECE 2022), 2022, Xishuangbanna, China

Abstract

As the most widely used clustering algorithm nowadays, the K-means algorithm has been applied in diverse fields. It is characterized with a fast calculation speed and a simple algorithm. It is, however, nonetheless beset by a number of problems. One difficulty is that choosing the starting center at random will have a negative influence on the clustering result. Second, outliers are susceptible to the K-means algorithm. Third, such an algorithm still has a significant time cost. To address these concerns, this work introduces the AGK Adaptive-GK method, which takes use of the grid clustering technique's benefits. Our AGK can accurately identify an initial center, increase the accuracy of the standard K-means method, and lower the algorithm's computation complexity. We did a thorough study of our AGK on a variety of data sets, and the findings show that it is accurate and efficient.

Citation Download Citation

An Zhou, Famao Mei, Zhenwei Gu, Hao Huang, and Siyuan Dong "AGK: the Adaptive Grid K-means algorithm", Proc. SPIE 12294, 7th International Symposium on Advances in Electrical, Electronics, and Computer Engineering, 122943U (19 October 2022); https://doi.org/10.1117/12.2641215

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