The incremental online k-means clustering algorithm and its application to color quantization

Abernathy, Amber; Celebi, M. Emre

doi:10.1016/j.eswa.2022.117927

Cited by 27 publications

(16 citation statements)

References 46 publications

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“…K-means is a partitional clustering algorithm called the K-means algorithm; it has unique advantages in extensive data analysis and information mining [ 20 ]. Figure 1 shows the workflow of the K-means clustering algorithm.…”

Section: Methods and Modelsmentioning

confidence: 99%

The Impact of the Implementation of International Law on Marine Environmental Protection on International Public Health Driven by Multi-Source Network Comment Mining

Yang

2023

IJERPH

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With the increase of people’s living space, global warming caused by the decrease of greening urban spaces and the serious decline of greenspace quality has led to extreme weather events and coastal erosion, which has become the biggest threat to the ocean and has also led to the occurrence of international public safety incidents. Therefore, it is of great practical significance to explore the tense relationship between the current marine environmental protection and global public safety for the development of an international healthy community. Firstly, this paper discusses the influence of implementing the international law of marine environmental protection on global public health after the reduction of green urban space and the decline of green space quality. Secondly, K-means and discrete particle swarm optimization algorithms are introduced and the particle swarm optimization-K-means clustering (PSO-K-means) algorithm is designed to screen and deal with the mapping relationship between latent variables and word sets about the impact of implementing the international marine ecological protection law on the international public health community in network data information. Moreover, the influencing factors are clustered and the scenarios are evaluated. The results show that the clustering analysis of the marine environment can promote the clustering of marine characteristic words. Meanwhile, the PSO-K-means algorithm can effectively cluster vulnerability data information. When the threshold is 0.45, the estimated recall rate of the corresponding model is 88.75%. Therefore, the following measures have been formulated, that is, increasing greening urban spaces and enhancing the quality of green space to enhance the protection of marine environment, which has practical reference value for realizing the protection of marine environment and the sustainable development of marine water resources and land resources.

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Section: Methods and Modelsmentioning

confidence: 99%

The Impact of the Implementation of International Law on Marine Environmental Protection on International Public Health Driven by Multi-Source Network Comment Mining

Yang

2023

IJERPH

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“…They proposed a hybrid approach by combining the K-means clustering algorithm and an artificial neural network (ANN) model and evaluated their approach through a case study of the Indian Ocean data. Abernathy et al [35] investigated the color quantization problem commonly used in image processing and proposed a partitional color quantization algorithm based on a binary splitting formulation of MacQueen's online K-means algorithm. Richardo et al [36] proposed a neutrosophic K-means algorithm by combining a classic K-means method with neutrosophy to analyze the earthquake data in Ecuador.…”

Section: Application Of K-means Clusteringmentioning

confidence: 99%

An Unsupervised Learning Approach for Analyzing Unsafe Pilot Operations Based on Flight Data

Qian

Chen

et al. 2022

Applied Sciences

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Flight safety is a hot topic in the aviation industry. Statistics show that safety incidents during landing are closely related to the flare phase because this critical period requires extensive pilot operations. Many airlines require that pilots should avoid performing any forward stick inputs during the flare. However, our statistical results from about 86,504 flights show that this unsafe pilot operation occasionally happens. Although several case studies were conducted previously, systematic research, especially based on a large volume of flight data, is still missing. This paper aims to fill this gap and provide more insights into the issue of pilots’ unsafe stick operations during the flare phase. Specifically, our work is based on the Quick Access Recorder (QAR) data, which consist of multivariate time-series data from various flight parameters. The raw data were carefully preprocessed, then key features were extracted based on flight expert experience, and a K-means clustering algorithm was utilized to divide the unsafe pilot operations into four categories. Based on the clustering results, we conducted an in-depth analysis to uncover the reasons for different types of unsafe pilot stick operations. In addition, extensive experiments were conducted to further investigate how these unsafe operations are correlated with different factors, including airlines, airports, and pilots. To the best of our knowledge, this is the first systematic study analyzing pilots’ unsafe forward stick operations based on a large volume of flight data. The findings can be used by airlines to design more targeted pilot training programs in the future.

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“…(2023) 033401 digital image processing. The first step consists in finding the most convenient color palette from the original histogram using thresholding levels [8,9] or clustering methods [10][11][12][13]. The second step, called pixel mapping, is usually achieved by performing a simple nearest color procedure.…”

Section: Introductionmentioning

confidence: 99%

A new spin on color quantization

2023

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We address the problem of image color quantization using a maximum entropy based approach. Focusing on pixel mapping we argue that adding thermal noise to the system yields better visual impressions than that obtained from a simple energy minimization. To quantify this observation, we introduce the coarse-grained quantization error, and seek the optimal temperature which minimizes this new observable. By comparing images with different structural properties, we show that the optimal temperature is a good proxy for complexity at different scales. Noting that the convoluted error is a key observable, we directly minimize it using a Monte Carlo algorithm to generate a new series of quantized images. Adopting an original approach based on the informativity of finite size samples, we are able to determine the optimal convolution parameter leading to the best visuals. Finally, we test the robustness of our method against changes in image type, color palette and convolution kernel.

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The incremental online k-means clustering algorithm and its application to color quantization

Cited by 27 publications

References 46 publications

The Impact of the Implementation of International Law on Marine Environmental Protection on International Public Health Driven by Multi-Source Network Comment Mining

The Impact of the Implementation of International Law on Marine Environmental Protection on International Public Health Driven by Multi-Source Network Comment Mining

An Unsupervised Learning Approach for Analyzing Unsafe Pilot Operations Based on Flight Data

A new spin on color quantization

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