Centerless Clustering

Pei, Shenfei; Chen, Huimin; Nie, Feiping; Wang, Rong; Li, Xuelong

doi:10.1109/tpami.2022.3150981

Cited by 12 publications

(5 citation statements)

References 43 publications

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“…Then Wan et al (2023b) obtained the final consensus matrix through projection matrix and the final result is obtained by using k-means on the consensus matrix. However, Wan et al (2023a) unified the new k-means to the optimization objective to obtain clustering results directly (Pei et al 2022). Kang et al (2020b) proposed to learn the graph of each view based on fixed anchors and then concatenate them.…”

Section: Algorithms Based On Matrix Factorizationmentioning

confidence: 99%

The methods for improving large-scale multi-view clustering efficiency: a survey

Yang,

Tan

2024

Artif Intell Rev

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The diversity and large scale of multi-view data have brought more significant challenges to conventional clustering technology. Recently, multi-view clustering has received widespread attention because it can better use different views’ consensus and complementary information to improve clustering performance. Simultaneously, many researchers have proposed various algorithms to reduce the computational complexity to accommodate the demands of large-scale multi-view clustering. However, the current reviews do not summarize from the perspective of reducing the computational complexity of large-scale multi-view clustering. Therefore, this paper outlines various high-frequency methods used in recent years to reduce the computational complexity of large-scale multi-view clustering, i.e. third-order tensor t-SVD, anchors-based graph construction, matrix blocking, and matrix factorization, and compares the corresponding algorithms based on several open datasets. Finally, the strengths and weaknesses of the current algorithm and the point of improvement are analyzed.

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Section: Algorithms Based On Matrix Factorizationmentioning

confidence: 99%

The methods for improving large-scale multi-view clustering efficiency: a survey

Yang,

Tan

2024

Artif Intell Rev

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“…In [23], considering the influence of noises and redundant features, a fuzzy c-means (FCM) with discriminative embedding to solve the problems including suboptimal results is proposed. In [24], k-means is combined with spectral clustering; then a clustering method is established to directly minimize the sum of the distances between points in the same cluster. To deal with the random initialization, a modified FCM is proposed in [25], a small non-negative value and other parameters are set to stabilize the initialization.…”

Section: Motivationmentioning

confidence: 99%

Peak‐valley period partition and abnormal time correction for time‐of‐use tariffs under daily load curves based on improved fuzzy c‐means

Wang,

Ma,

Ling

et al. 2023

IET Generation Trans & Dist

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Peak‐valley period partition of load curve is a key aspect of time‐of‐use (ToU) tariff to improve power load characteristics, such as shifting peak loads towards valley time periods. Fuzzy clustering algorithm is an effective and popular method commonly used to solve the peak‐valley period partition of load curves, but it still encounters the difficulty of dividing some data within the boundary regions of different time periods. Therefore, this paper presents a peak‐valley period partition and abnormal time correction scheme for ToU tariffs under typical daily load curves based on improved fuzzy C‐means (FCM) clustering algorithm. In order to improve the accuracy of peak‐valley period partition, modified fuzzy membership functions are proposed to improve the initialization of FCM clustering, and a loss function‐based method is presented for calculating the fuzzy parameters of those membership functions. To resolve the problem of abnormal time partitioning within the boundaries of different time periods, an abnormal time period recognition model and a correction model based on fuzzy subsethood are proposed to obtain the final corrected peak‐valley time period partitioning results. On the MATLAB R2020b platform, the effectiveness of the proposed method is verified through two real daily load curves with a time resolution of 5 min.

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“…Pei et al introduced a new clustering technique [21]. The authors used 18 real-world datasets, with most of them being face images datasets.…”

Section: A Related Workmentioning

confidence: 99%

Comparison of Machine Learning Classification and Clustering Algorithms for TV Commercials Detection

Abdelfattah,

Joshi

2023

IEEE Access

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One of the essential aspects of broadcast monitoring is to detect and consequently extract commercial blocks in telecast news videos. The research carried out until now have based their work almost entirely on preconceived characteristics that are associated with a channel. With the advertisers constantly looking to work around the existing policies, the reliance on the nature of channels during an advertisement does not suffice. The other approach towards identifying a commercial is by frequentist approach. However, it is often the case that sponsored programs and other programs share similar time in any specified hour, rendering the frequentist approach almost useless in the process. As such, this paper uses machine learning based approach which is more generic and can employ inherent differences that commercials have over their non-commercial counterparts for classifying and clustering commercials in the news videos. The datasets which contain 90 hours of recordings from five different news channels from US, England and India have been used to train and test nine different classifiers -K Neighbors, Support Vector Machine, Decision Tree, Random Forests, Ada Boost, Gradient Boost, Gaussian NB, Linear Discriminant Analysis, and Quadratic Discriminant Analysisand five different clustering algorithms -K Means, Agglomerative, Birch, Mini-Batch K Means, and Gaussian Mixture. Our results show that the Random Forests outperforms all the other classifiers used with the highest F1 score of 97% and median time of 31 seconds to train and test on each of these datasets that consists of features of shots extracted from 18 hours of video. Similarly, Mini Batch K Means was found to perform the best for clustering with the highest Silhouette Score of 0.7 and median time of 30 seconds to form clusters of news and commercials.

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Centerless Clustering

Cited by 12 publications

References 43 publications

The methods for improving large-scale multi-view clustering efficiency: a survey

The methods for improving large-scale multi-view clustering efficiency: a survey

Peak‐valley period partition and abnormal time correction for time‐of‐use tariffs under daily load curves based on improved fuzzy c‐means

Comparison of Machine Learning Classification and Clustering Algorithms for TV Commercials Detection

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