Identifying key nodes of the network helps design network protection policies and improves network robustness and reliability. This paper proposes a network node grouping algorithm and a grouping performance evaluatio...
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clustering by fast search and find of density peaks(CFSFDP) has the advantages of a novel idea, easy implementation, and efficient clustering. It has been widely recognized in various fields since it was proposed in S...
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clustering by fast search and find of density peaks(CFSFDP) has the advantages of a novel idea, easy implementation, and efficient clustering. It has been widely recognized in various fields since it was proposed in Science in 2014. The CFSFDP algorithm also has certain limitations, such as non-unified sample density metrics defined by cutoff distance, the domino effect for the assignment of remaining samples triggered by unstable assignment strategy, and the phenomenon of picking wrong density peaks as cluster centers. We propose reverse-nearest-neighbor-based clustering by fast search and find of density peaks(RNN-CFSFDP) to avoid these shortcomings. We redesign and unify the sample density metric by introducing reverse nearest neighbor. The newly defined local density metric and the K-nearest neighbors of each sample are combined to make the assignment process more robust and alleviate the domino effect. A cluster fusion algorithm is proposed, which further alleviates the domino effect and effectively avoids the phenomenon of picking wrong density peaks as cluster centers. Experimental results on publicly available synthetic data sets and real-world data sets show that in most cases, the proposed algorithm is superior to or at least equivalent to the comparative methods in clustering performance. The proposed algorithm works better on manifold data sets and uneven density data sets.
Recently, clustering techniques gained more importance due to huge range of applications in the field of data mining, pattern recognition, data clustering, bio informatics and many other applications. In this paper, a...
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The contribution main from this research is modularity and better processing time in detecting community by using K-1 coloring. Testing performed on transaction datasets remittance on P2P platforms where the Louvain C...
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In this article a method is proposed based on a spatial clustering algorithm, in order to better realize the design of ecological land consolidation planning and regulation mode. Considering the GIS methods such as sp...
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In this article a method is proposed based on a spatial clustering algorithm, in order to better realize the design of ecological land consolidation planning and regulation mode. Considering the GIS methods such as spatial clustering algorithm and least resistance model, the proposed model selects typical projects and put forward ecological land consolidation planning scheme. The unified engineering construction forms and rigid engineering construction standards aims at regularization and rigidization of traditional land consolidation by means of high-intensity engineering construction. Although in the process of stabilizing the amount of cultivated land and improving agricultural production conditions, played a positive role. However, under the current requirements of ecological civilization construction, there is an urgent need for transformation. Through the improvement of ecological land, the ecological improvement is realized, and the need for landscape improvement is foremost requirement. The study is guided by the theory of landscape ecology, according to the general idea of "landscape pattern evaluation-land remediation function zoning-corridor pattern optimization-patch matrix optimization". The experimental results are generated using Fragstates software for calculating various indicators. The results show that, before and after renovation, the landscape types with the largest landscape ecological security index LESi were all cultivated land, and the smallest one was forest land. Except for the river landscape ecological security index, which dropped by 9.84%, the ecological security of other types of landscapes improved. Among them, the largest increase was road (121.29%), followed by forest land (43.10%). According to formulas (1) and (2), the landscape safety index LES of the project area is 0.45 before the renovation, and 0.61 after the renovation. An increase of 35.56% is observed which shows that through the ecological land consolidation project area
This paper has emphasized several sounds for Bird Species Recognition based on their vocalization. Although various techniques have been designed with good equipment for identifying different birds’ sounds, still it ...
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In the evolving field of precision agriculture, accurate monitoring of vineyard health using advanced technologies is crucial for efficient resource management and addressing climate change challenges. Optimized disea...
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In the evolving field of precision agriculture, accurate monitoring of vineyard health using advanced technologies is crucial for efficient resource management and addressing climate change challenges. Optimized disease detection methods enhance efficiency, sustainability and economic viability, making non-destructive health assessment vital for modern agricultural practices. This study aims to differentiate grapevine varieties based on their spectral characteristics using multispectral imaging. Focusing on grapevine canopies within a vineyard in the Attica region of Greece, this research proposes a methodology for aerial multispectral images exploitation captured over two consecutive years, namely 2022 and 2023. Unlike typical vineyards with limited grape varieties, the study area included over 70 varieties, each with relatively small sample sizes. Classification algorithms were employed to separate vines from soil and shadows, with the Maximum Likelihood algorithm achieving 98.79% and 90.53% accuracy for the 2022 and 2023 images, respectively. Vegetation indices were applied to assess vine health, chlorophyll content and canopy density. Among seven indices, the Chlorophyll Vegetation Index (CVI) and Vegetation Ratio Index (RVI) were selected due to their low correlation. Six clustering algorithms were tested, with the Bisecting K-means algorithm proving the most effective, achieving a silhouette value of 0.41. Comparative analysis between the 2022 and 2023 clusters revealed that 34 vine varieties maintained stable health, 24 improved and 15 worsened. This study underscores the potential of multispectral imaging and clustering algorithms in vineyard management, offering insights to optimize cultivation practices based on spectral data.
The importance of dealing with big data is further increasing, as machine learning (ML) systems obtain useful knowledge from big datasets. However, using all data is practically prohibitive because of the massive size...
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The importance of dealing with big data is further increasing, as machine learning (ML) systems obtain useful knowledge from big datasets. However, using all data is practically prohibitive because of the massive sizes of the datasets, so summarizing them by centers obtained from k-center clustering is a promising approach. We have two concerns here. One is fairness, because if the summary does not have some specific groups, subsequent applications may provide unfair results for the groups. The other is the presence of outliers, and if outliers dominate the summary, it cannot be useful. To overcome these concerns, we address the problem of fair k-center clustering with outliers. Although prior works studied the fair k-center clustering problem, they do not consider outliers. This paper yields a linear time algorithm that satisfies the fairness constraint of our problem and probabilistically guarantees the almost 3-approximation bound. Its empirical efficiency and effectiveness are also reported.
Scholars in the machine learning community have recently focused on analyzing the fairness of learning models, including clustering algorithms. In this work we study fair clustering in a probabilistic (soft) setting, ...
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Scholars in the machine learning community have recently focused on analyzing the fairness of learning models, including clustering algorithms. In this work we study fair clustering in a probabilistic (soft) setting, where observations may belong to several clusters determined by probabilities. We introduce new probabilistic fairness metrics, which generalize and extend existing non-probabilistic fairness frameworks and propose an algorithm for obtaining a fair probabilistic cluster solution from a data representation known as a fairlet decomposition. Finally, we demonstrate our proposed fairness metrics and algorithm by constructing a fair Gaussian mixture model on three real-world datasets. We achieve this by identifying balanced micro-clusters which minimize the distances induced by the model, and on which traditional clustering can be performed while ensuring the fairness of the solution.
Image coding technologies are essential to use communication channels effectively. We have been studied vector quantization, because it does not cause deterioration in image quality in a high compression region and al...
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