A comparative study of K-Means, K-Means++ and Fuzzy C-Means clustering algorithms

Kapoor, Akanksha; Singhal, Abhishek

doi:10.1109/ciact.2017.7977272

Cited by 71 publications

(31 citation statements)

References 12 publications

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“…The utilized two‐stage clustering method allowed better representation of color of human gingiva. The first stage of Fuzzy C‐means algorithm is a very important improvement over other traditional clustering algorithms, as it considers the uncertainty over a sample potentially belonging to multiple clusters, and is traditionally used in color and image computer‐based applications . The second stage of the algorithm performed a double optimization procedure, in which clusters obtained after the FCM execution, were translated in order to decrease the coverage of the most separated gingiva samples.…”

Section: Discussionmentioning

confidence: 99%

Gingival shade guides: Colorimetric and spectral modeling

Ghinea

Herrera

Pérez

et al. 2018

J Esthet Restor Dent

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Providing a shade guide model with a small number of tabs and a coverage error lower than the 50:50% acceptability threshold would be an optimal solution for shade matching in dentistry. However, no actual gingival or tooth shade guide complies with this. The clustering method, with optimization of both Coverage Error and Maximal Error and spectral clustering that enables more reliable color formulation of cluster representatives of shade guide models, represents an advance when it comes to computer modeling in dentistry.

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Section: Discussionmentioning

confidence: 99%

Gingival shade guides: Colorimetric and spectral modeling

Ghinea

Herrera

Pérez

et al. 2018

J Esthet Restor Dent

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“…Based on the fuzzy C-means algorithm [28], we integrate driving behavior characteristics and introduce the log fuzzy C-means algorithm (LFCM) to guide the clustering of driving characteristics in the style space. The LFCM algorithm is shown in Figure 1.…”

Section: Acquisition Of Individualized Driving Stylesmentioning

confidence: 99%

Path Planning Strategy for Vehicle Navigation Based on User Habits

et al. 2018

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Vehicle navigation is widely used in path planning of self driving travel, and it plays an increasing important role in people's daily trips. Therefore, path planning algorithms have attracted substantial attention. However, most path planning methods are based on public data, aiming at different driver groups rather than a specific user. Hence, this study proposes a personalized path decision algorithm that is based on user habits. First, the categories of driving characteristics are obtained through the investigation of public users, and the clustering results corresponding to the category space are obtained by log fuzzy C-means clustering algorithm (LFCM) based on the driving information contained in the log trajectories. Then, the road performance personalized quantization algorithm evaluation is proposed to evaluate roads from the user's field of vision. Finally, adaptive ant colony algorithm is improved and used to validate the path planning based on the road performance personalized values. Results show that the algorithm can meet the personalized requirements of the user path selection in the path decision.

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“…erefore, it was necessary to improve K-means++ for use of large datasets, leading to the development of a more efficient parallel version of K-means++ [19]. Another study [20] addressed the problem by using a sorted dataset, which is claimed to decrease the running time. e literature also includes many studies on enhancing the accuracy or the performance of K-means++, but none on visualizing a 2D map for the clusters of this successful algorithm.…”

Section: Related Workmentioning

confidence: 99%

Novel Two-Dimensional Visualization Approaches for Multivariate Centroids of Clustering Algorithms

Doğan

Dalkılıç

Birant

et al. 2018

Scientific Programming

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The dimensionality reduction and visualization problems associated with multivariate centroids obtained by clustering algorithms are addressed in this paper. Two approaches are used in the literature for the solution of such problems, specifically, the self-organizing map (SOM) approach and mapping selected two features manually (MS2Fs). In addition, principle component analysis (PCA) was evaluated as a component for solving this problem on supervised datasets. Each of these traditional approaches has drawbacks: if SOM runs with a small map size, all centroids are located contiguously rather than at their original distances according to the high-dimensional structure; MS2Fs is not an efficient method because it does not take features outside of the method into account, and lastly, PCA is a supervised method and loses the most valuable feature. In this study, five novel hybrid approaches were proposed to eliminate these drawbacks by using the quantum genetic algorithm (QGA) method and four feature selection methods, Pearson’s correlation, gain ratio, information gain, and relief methods. Experimental results demonstrate that, for 14 datasets of different sizes, the prediction accuracy of the proposed weighted clustering approaches is higher than the traditional K-means++ clustering approach. Furthermore, the proposed approach combined with K-means++ and QGA shows the most efficient placements of the centroids on a two-dimensional map for all the test datasets.

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A comparative study of K-Means, K-Means++ and Fuzzy C-Means clustering algorithms

Cited by 71 publications

References 12 publications

Gingival shade guides: Colorimetric and spectral modeling

Gingival shade guides: Colorimetric and spectral modeling

Path Planning Strategy for Vehicle Navigation Based on User Habits

Novel Two-Dimensional Visualization Approaches for Multivariate Centroids of Clustering Algorithms

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