Volume 30 Issue 5
Sep.  2021
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ZHAO Boxiang, WANG Shuliang, LIU Chuanlu. STATE: A Clustering Algorithm Focusing on Edges Instead of Centers[J]. Chinese Journal of Electronics, 2021, 30(5): 902-908. doi: 10.1049/cje.2021.07.001
Citation: ZHAO Boxiang, WANG Shuliang, LIU Chuanlu. STATE: A Clustering Algorithm Focusing on Edges Instead of Centers[J]. Chinese Journal of Electronics, 2021, 30(5): 902-908. doi: 10.1049/cje.2021.07.001

STATE: A Clustering Algorithm Focusing on Edges Instead of Centers

doi: 10.1049/cje.2021.07.001
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This work is supported by Science and Technology Innovation Research Project of the Ministry of Science and Technology of China (No.ZLY201970, No.ZLY201976-02) and Special Program for Technical Support of the State Administration for Market Regulation (No.2020YJ037).

  • Received Date: 2020-02-28
    Available Online: 2021-09-02
  • With the expansion of data scale and the increase in data complexity, it is particularly important to accurately identify clusters and efficiently save clustering results. To address this, we propose a novel clustering algorithm, Shape clustering based on data field (STATE), which can quickly identify clusters of arbitrary shapes and greatly reduce the storage space of clustering results in any datasets without reducing the accuracy. STATE mainly focuses on finding the edges of clusters and directions of edges instead of clustering centers through the data field. The results of STATE are presented as the edges of clusters without data objects inside clusters and without noise. Extensive experiments show that STATE can recognize complex data distribution in noisy environments without discrimination and greatly save the storage space of clustering results. When it is applied in a real-world scene, facial feature extraction, STATE can recognize eyes, nose, mouth, eyebrows and facial contours automatically without calibrating key features or training. Using the extracted facial features, we achieve facial recognition with high accuracy.
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