The global Minmax k-means algorithm

Wang, Xiaoyan; Bai, Yanping

doi:10.1186/s40064-016-3329-4

Cited by 43 publications

(19 citation statements)

References 17 publications

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“…In this section, we first present some segmentation results on synthetic and real images from our proposed segmentation method using some vectorial orders and compared with those of the K-means method [17]. Then a quantitative evaluation through supervised evaluation methods and unsupervised evaluation of the quality of the segmentation through different vectorial orders with respect to K-means is performed.…”

Section: Segmentation Results and Evaluation Of The Proposed Approachmentioning

confidence: 99%

A Semi-Vectorial Hybrid Morphological Segmentation of Multicomponent Images Based on Multithreshold Analysis of Multidimensional Compact Histogram

Kouassi¹,

Ouattara²,

Okaingni³

et al. 2017

OJAppS

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In this work, we propose an original approach of semi-vectorial hybrid morphological segmentation for multicomponent images or multidimensional data by analyzing compact multidimensional histograms based on different orders. Its principle consists first of segment marginally each component of the multicomponent image into different numbers of classes fixed at K. The segmentation of each component of the image uses a scalar segmentation strategy by histogram analysis; we mainly count the methods by searching for peaks or modes of the histogram and those based on a multi-thresholding of the histogram. It is the latter that we have used in this paper, it relies particularly on the multi-thresholding method of OTSU. Then, in the case where i) each component of the image admits exactly K classes, K vector thresholds are constructed by an optimal pairing of which each component of the vector thresholds are those resulting from the marginal segmentations. In addition, the multidimensional compact histogram of the multicomponent image is computed and the attribute tuples or 'colors' of the histogram are ordered relative to the threshold vectors to produce (K + 1) intervals in the partial order giving rise to a segmentation of the multidimensional histogram into K classes. The remaining colors of the histogram are assigned to the closest class relative to their center of gravity. ii) In the contrary case, a vectorial spatial matching between the classes of the scalar components of the image is produced to obtain an over-segmentation, then an interclass fusion is performed to obtain a maximum of K classes. Indeed, the relevance of our segmentation using unsupervised and supervised quantitative segmentation evaluation criteria. So the robustness of our method relatively to noise has been tested.

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Section: Segmentation Results and Evaluation Of The Proposed Approachmentioning

confidence: 99%

A Semi-Vectorial Hybrid Morphological Segmentation of Multicomponent Images Based on Multithreshold Analysis of Multidimensional Compact Histogram

Kouassi¹,

Ouattara²,

Okaingni³

et al. 2017

OJAppS

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show abstract

“…In order to demonstrate that our AGDNMF method improves the clustering performance, we compare it with the following algorithm, such as other K-means clustering method [35], PCA [9], non-negative matrix factorization NMF [10], graph regularized nonnegative matrix factorization (GNMF) [34], constrained nonnegative matrix factorization (CNMF) [28], robust graph regularized nonnegative matrix factorization (RGNMF) [36], discriminative nonnegative matrix factorization (DNMF) [26].…”

Section: B Comparison Methodsmentioning

confidence: 99%

Adaptive Graph Regularization Discriminant Nonnegative Matrix Factorization for Data Representation

Zhang

Wang

et al. 2019

IEEE Access

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Nonnegative matrix factorization, as a classical part-based representation method, has been widely used in pattern recognition, data mining and other fields. However, the traditional nonnegative matrix factorization directly factoring decomposes the original data, and the original data often contains a lot of redundancy and noise, which seriously affect the subsequent processing of the data. In this work, we propose an adaptive graph regularization discriminant nonnegative matrix factorization (AGDNMF) for image clustering. The AGDNMF algorithm makes full use of local structure information and a small amount of label information. In AGDNMF, the local structure information can be more accurate and the label information can prevent the points with the same label from being merged into one point. These two items are combined into the objective function of NMF. In addition, we provide the update rules for the corresponding optimization functions and prove its convergence. A large number of experiments on different data sets show that the proposed algorithm has good clustering performance.

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“…Firstly, a sample was randomly selected as the first clustering center C 0 ; secondly, calculating the distance between each sample point and C 0 , the farthest sample point served as the second clustering center C 1 ; calculate the distance between the rest of the sample points and the cluster center C 0 and C 1 respectively; choose the smaller distance, and then choose the largest distance from these distance as the third clustering center. By analogy, K clustering centers were finally found [8].…”

Section: Selection Of Initial Clustering Centersmentioning

confidence: 99%

Virtual machine scheduling strategy based on machine learning algorithms for load balancing

Xin

Liú

et al. 2019

J Wireless Com Network

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With the rapid increase of user access, load balancing in cloud data center has become an important factor affecting cluster stability. From the point of view of green scheduling, this paper proposed a virtual machine intelligent scheduling strategy based on machine learning algorithm to achieve load balancing of cloud data center. Firstly, a load forecasting algorithm based on genetic algorithm (SVR_GA), k-means clustering algorithm based on optimized min-max, and adaptive differential evolution algorithm (ESA_DE) to enhance local search ability are proposed to solve the load imbalance problem in cloud data center. The experimental results showed that compared with other classical algorithms, the proposed virtual machine scheduling strategy reduces the number of virtual machine migration by 94.5% and the energy consumption of cloud data center by 49.13%.

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The global Minmax k-means algorithm

Cited by 43 publications

References 17 publications

A Semi-Vectorial Hybrid Morphological Segmentation of Multicomponent Images Based on Multithreshold Analysis of Multidimensional Compact Histogram

A Semi-Vectorial Hybrid Morphological Segmentation of Multicomponent Images Based on Multithreshold Analysis of Multidimensional Compact Histogram

Adaptive Graph Regularization Discriminant Nonnegative Matrix Factorization for Data Representation

Virtual machine scheduling strategy based on machine learning algorithms for load balancing

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