Analysis and improvement of evaluation indexes for clustering results

Zhong, Hao; Zhang, Huibing; Jia, Fei

doi:10.4108/eai.9-10-2017.163211

Cited by 7 publications

(4 citation statements)

References 31 publications

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Section: Methodsmentioning

confidence: 99%

“…The multicolor fabric has complex colors and patterns, and it is extremely inefficient to identify the color number of the fabric manually, so the internal evaluation index silhouette coefficient (SC) is used to determine the number of cluster centers automatically in this paper. 26 In the EDSC algorithm, first the coefficient matrix is calculated based on the input data and the affinity matrix is constructed; Then, the input data were divided into three parts according to the affinity matrix: noise data, noise-free data and outliers. It converts the subspace clustering into an optimization problem of each data point, which is divided into the following cases generally.…”

Section: The Edsc Algorithmmentioning

confidence: 99%

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Color segmentation of multicolor porous printed fabrics by conjugating SOM and EDSC clustering algorithms

Qian

Wang

Huang

et al. 2022

Textile Research Journal

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In order to achieve accurate color segmentation of printed fabrics, the color segmentation algorithm combining the self-organizing maps neural network and the efficient dense subspace clustering was proposed in this paper. After pre-processing of the fabric image, the primary clustering was implemented by the self-organizing maps algorithm, then the secondary clustering was done by the efficient dense subspace clustering algorithm. The optimal silhouette coefficient is introduced into the clustering process of the efficient dense subspace clustering algorithm to determine the number of clustering centers automatically. Finally, by the post-processing including gray-scale transformation, binarization and open operation, the mis-segmentation of edge color was eliminated, making the algorithm more suitable for industrial application. Experiments were carried out and results show that the algorithm proposed in this paper can recognize the color of small areas accurately and segment the color of complex printed fabric images. The color segmented results of 20 printed fabrics show that the accuracy of the algorithm proposed in this paper reaches 88.3%.

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Section: Methodsmentioning

confidence: 99%

Section: The Edsc Algorithmmentioning

confidence: 99%

Color segmentation of multicolor porous printed fabrics by conjugating SOM and EDSC clustering algorithms

Qian

Wang

Huang

et al. 2022

Textile Research Journal

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“…These metrics provide a quantitative basis for comparison and help illustrate why DEC outperformed traditional methods. These metrics are: Inertia, Silhouette Score, Davies-Bouldin Index, Calinski-Harabasz Index [12]. The DEC algorithm stands out with its superior performance, as evidenced by the highest Silhouette Score and Calinski-Harabasz Index, alongside the lowest Davies-Bouldin Index.…”

Section: Communities Clustering and Identificationmentioning

confidence: 99%

Strategic Planning of Rural Telecommunication Infrastructure: A Multi-Source Data Fusion and Optimization Model

Chaabane,

Réjichi,

Ben Salem

et al. 2024

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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Abstract. The United Nations has underscored the critical role of digital connectivity by integrating it into several sustainable development goals, with the ambition for nations worldwide to achieve comprehensive access by 2030. Over thousand million people, primarily in rural areas, are disconnected from the digital world, highlighting the urgent need for viable and sustainable telecommunications solutions. These areas are characterized by sparse populations and lower economic levels, presenting great challenges for connectivity. This work introduces a strategy for enhancing rural telecommunication planning using geospatial and remote sensing data, deep learning-based clustering techniques, network graphs, and terrain analysis. The objective is to develop an optimal network topology and identify prime locations for telecommunications infrastructure, such as towers or relay stations. The methodology begins with the application of an adapted Deep Embedded Clustering (DEC) technique to identify community boundaries accurately. Then, it combines geospatial data (such as roads, terrain slope, flatness, etc.) and remote sensing data (vegetation, waterways, etc.) through an optimization algorithm. This process aims to determine the most suitable sites for infrastructure placement and the best network topology for connecting these areas. The study focuses on the region of Congo, offering a detailed case study on the application of this approach. Experimental results are presented to demonstrate the effectiveness of the proposed telecommunications expansion strategy.

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“…To inform this determination, k-means clustering was performed for a range of numbers of clusters and a silhouette coefficient was calculated for each number of clusters. The silhouette coefficient is a measure of cluster validity that measures the distance between samples within the same cluster and distances between samples in separate clusters, with higher values indicating more effective clustering (Zhong, Zhang, & Jia, 2020). Charts were created to see the silhouette coefficients for each number of clusters for the primary and secondary clusters, with the number of clusters being selected from observable peaks in the charts.…”

Section: Clusteringmentioning

confidence: 99%

Open Geodemographics and Crime: A Case Study Of Toronto

Larsen

2024

Preprint

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<p>Geodemographic segmentation systems are ubiquitous in commercial applications for marketing but academic research on geodemographics shows limited focus on crime. A criticism of geodemographic segmentation systems is their closed nature, with many systems only accessible through commercial means, making them inaccessible for general public access. Using a more open approach that provides access to geodemographic systems, methodology, and applications would increase the availability and potential use of geodemographic solutions. The research presented in this paper puts forward a geodemographic segmentation system for Toronto that uses crime data in the creation of its clusters and provides the system and the methodology open to the public. This serves the dual purpose of providing a system for others to use for their own analysis on crime within Toronto and providing the methodology for others to create their own system. This research found that crime data is well suited to a geodemographic segmentation approach. Further research can apply these open data techniques to other cities or even countries.</p>

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Analysis and improvement of evaluation indexes for clustering results

Cited by 7 publications

References 31 publications

Color segmentation of multicolor porous printed fabrics by conjugating SOM and EDSC clustering algorithms

Color segmentation of multicolor porous printed fabrics by conjugating SOM and EDSC clustering algorithms

Strategic Planning of Rural Telecommunication Infrastructure: A Multi-Source Data Fusion and Optimization Model

Open Geodemographics and Crime: A Case Study Of Toronto

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