Silhouette + attraction: A simple and effective method for text clustering

Errecalde, Marcelo Luis; Cagnina, Leticia; Rosso, Paolo

doi:10.1017/s1351324915000273

“…By comparing the BCALoD algorithm, K-means clustering, and mixed Gaussian clustering, the relative performance of the BCALoD algorithm was determined. In K-means clustering, the silhouette coefficient 17 is used to calculate the clustering and separation degrees, and the maximum value of the silhouette coefficient is selected as the number of clusters, which completes K-means clustering. In mixed Gaussian clustering, the Bayesian information criterion (BIC) 18 is used to select the number of clusters, and the minimum value of the BIC is selected as the data number of clusters.…”

Section: Resultsmentioning

confidence: 99%

A novel bidirectional clustering algorithm based on local density

Lyu

¹

,

Wu

²

,

Hu

³

2021

Sci Rep

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With the widely application of cluster analysis, the number of clusters is gradually increasing, as is the difficulty in selecting the judgment indicators of cluster numbers. Also, small clusters are crucial to discovering the extreme characteristics of data samples, but current clustering algorithms focus mainly on analyzing large clusters. In this paper, a bidirectional clustering algorithm based on local density (BCALoD) is proposed. BCALoD establishes the connection between data points based on local density, can automatically determine the number of clusters, is more sensitive to small clusters, and can reduce the adjusted parameters to a minimum. On the basis of the robustness of cluster number to noise, a denoising method suitable for BCALoD is proposed. Different cutoff distance and cutoff density are assigned to each data cluster, which results in improved clustering performance. Clustering ability of BCALoD is verified by randomly generated datasets and city light satellite images.

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“…By comparing the BCALoD algorithm, K-means clustering, and mixed Gaussian clustering, the relative performance of the BCALoD algorithm was determined. In Kmeans clustering, the silhouette coe cient [16] is used to calculate the clustering and separation degrees, and the maximum value of the silhouette coe cient is selected as the number of clusters, which completes K-means clustering. In mixed Gaussian clustering, the Bayesian information criterion (BIC) [17] is used to select the number of clusters, and the minimum value of the BIC is selected as the data number of clusters.…”

Section: Resultsmentioning

confidence: 99%

A novel bidirectional clustering algorithm based on local density

Lv

¹

,

Wu

²

,

Hu

³

2021

Preprint

0

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With the widely application of cluster analysis, the number of clusters is gradually increasing, as is the difficulty in selecting the judgment indicators of cluster numbers. Also, small clusters are crucial to discovering the extreme characteristics of data samples, but current clustering algorithms focus mainly on analyzing large clusters. In this paper, a bidirectional clustering algorithm based on local density (BCALoD) is proposed. BCALoD establishes the connection between data points based on local density, can automatically determine the number of clusters, is more sensitive to small clusters, and can reduce the adjusted parameters to a minimum. On the basis of the robustness of cluster number to noise, a denoising method suitable for BCALoD is proposed. Different cutoff distance and cutoff density are assigned to each data cluster, which results in improved clustering performance. Clustering ability of BCALoD is verified by randomly generated datasets and city light satellite images.

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“…Two measures are employed to estimate the clustering effect of ICs and PCs: SC and Kmeans. SC is a classic validity measure for clustering problems [23]. The SC combines two key aspects to determine the quality of a cluster-cohesion and separation.…”

Section: Resultsmentioning

confidence: 99%

Discriminative power of independent component analysis applied to an electronic nose

Sun

¹

,

Du

²

,

Zheng

³

2019

Meas. Sci. Technol.

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Electronic noses have been developed to analyze gas composition in many applications. The potential analytical power of an electronic nose needs to be investigated with regard to detection in a complicated gas environment. To date, principal component analysis (PCA) has been a popular analytical tool for use with electronic noses; however, PCA reaches its limits when the measurement data exhibit nonGaussian behavior. Components extracted by independent component analysis (ICA) are expected to have a high discriminative power, conducive to improving the performance of an electronic nose. To verify this hypothesis, the performance of principal components (PCs) and independent components (ICs) in classification and regression tasks is compared. The data were obtained from a chemical gas sensor array for volatile organic compounds (VOCs). In the classification task, silhouette coefficients and Kmeans are employed to measure the performance of ICs and PCs. ICs have evident classification advantages over PCs because of their higher accuracy. In the regression task, ICs and PCs are applied via a neural network to predict the concentration of four VOCs. The errors in the prediction are calculated and analyzed, and it is found that ICs continue to outperform PCs during the regression task. The results of this study demonstrate the potential and ability of ICA to extract features when used with an electronic nose in a complicated gas environment.

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Silhouette + attraction: A simple and effective method for text clustering

Cited by 6 publications

References 56 publications

A novel bidirectional clustering algorithm based on local density

A novel bidirectional clustering algorithm based on local density

A novel bidirectional clustering algorithm based on local density

Discriminative power of independent component analysis applied to an electronic nose

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