Explore unlabeled big data learning to online failure prediction in safety-aware cloud environment

Zhao, Jia; Ding, Yan; Zhai, Yunan; Jiang, Yuqiang; Zhai, Yujuan; Hu, Ming

doi:10.1016/j.jpdc.2021.02.025

Cited by 4 publications

(2 citation statements)

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“…Clustering analysis is a technique of grouping unlabeled data into different clusters based on their similarities [1][2][3][4]. This approach is essential in the areas of unsupervised machine learning, data mining, pattern recognition, image analysis, and much more [4][5][6][7].…”

Section: Introductionmentioning

confidence: 99%

“…The unlabeled dataset is essentially divided into labeled groups regardless of the presence of similar patterns [8,9]. These labeled clusters can be used simply to process and analyze large and complex datasets in many applications, such as healthcare [10,11], renewable energy [12,13], image segmentation [14][15][16], data analysis [1,9], social network analysis [17,18], security [19,20], finance and business [21,22], and much more. Many studies on clustering algorithms have been conducted.…”

Section: Introductionmentioning

confidence: 99%

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Fast Component Density Clustering in Spatial Databases: A Novel Algorithm

Bataineh

2022

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Clustering analysis is a significant technique in various fields, including unsupervised machine learning, data mining, pattern recognition, and image analysis. Many clustering algorithms are currently used, but almost all of them encounter various challenges, such as low accuracy, required number of clusters, slow processing, inability to produce non-spherical shaped clusters, and unstable performance with respect to data characteristics and size. In this research, a novel clustering algorithm called the fast component density clustering in spatial databases (FCDCSD) is proposed by utilizing a density-based clustering technique to address the aforementioned existing challenges. First, from the smallest to the largest point in the spatial field, each point is labeled with a temporary value, and the adjacent values in one component are stored in a set. Then, all sets with shared values are merged and resolved to obtain a single value that is representative of the merged sets. These values represent final cluster values; that is, the temporary equivalents in the dataset are replaced to generate the final clusters. If some noise appears, then a post-process is performed, and values are assigned to the nearest cluster based on a set of rules. Various synthetic datasets were used in the experiments to evaluate the efficiency of the proposed method. Results indicate that FCDCSD is generally superior to affinity propagation, agglomerative hierarchical, k-means, mean-shift, spectral, and density-based spatial clustering of applications with noise, ordering points for identifying clustering structures, and Gaussian mixture clustering methods.

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