An improved electrical anomaly detection algorithm based on density peak clustering

Mi, Gaina; Du, Tao; Zhou, Jin

doi:10.1109/auteee52864.2021.9668785

Cited by 1 publication

(1 citation statement)

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“…There are many clustering algorithms, which can be roughly divided into several categories according to their working mechanisms: density-based, such as DPC algorithm [2], DBSCAN algorithm [3]; model-based, such as Gaussian mixture model (GMM) algorithm [4]; Fuzzy-based clustering, such as FCM algorithm [5]; hierarchy-based, such as BIRCH algorithm [6], CURE algorithm [7]; grid-based, such as STING algorithm, CLIQUE algorithm; partition-based, such as K-means Algorithms [8]; graph-based, such as spectral clustering algorithms [9]. Each clustering algorithm has its advantages and disadvantages, and has its own applicable scenarios.…”

Section: Selection Of Clustering Algorithmsmentioning

confidence: 99%

Visualization-based clustering algorithm experimental teaching design

He¹,

He²,

Hu³

2023

Third International Conference on Intelligent Computing and Human-Computer Interaction (ICHCI 2022)

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Clustering is an important part of artificial intelligence and is widely used in data mining, pattern recognition, natural language processing, computer vision and other aspects. Clustering algorithms are complex and difficult to master. In order to make learning easier and more interesting, a visual clustering experiment is designed. The first is the introduction of clustering algorithms and clustering indicators; then basic experiments are designed to show the working principle and clustering characteristics of each algorithm through the visualization of the clustering process and the visualization of the clustering results, and introduce the self-generated artificial data to the method to verify the characteristics of the algorithm; the last is the application experiment, through the face clustering and image segmentation experiments to improve the students' interest in learning.

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