Prediction model for lightning nowcasting based on DBSCAN

R, Hou; Zhu, Bin; Feng, Meichen; Shi, Xinming; Lu, Yu

doi:10.3724/sp.j.1087.2012.00847

Cited by 2 publications

(1 citation statement)

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“…As early as 1987, Dubes explored to decide the group of targeted clusters for the k-means method by Monte Carlo experiments (Ding et al, 2015). Given a data set, the algorithm first creates sequences of partitions and then compares adjacent partitions in terms of internal indices, such as Davies and Bouldin index and a new modification of the Hubert Γ statistics (MH) (Hou et al, 2013). In case of MH, if there is a significant difference between two adjacent partitions, then one of the two partitions with much higher value of MH will be the optimal clustering result.…”

Section: Parameter Reductionmentioning

confidence: 99%

IDCUP Algorithm to Classifying Arbitrary Shapes and Densities for Center-based Clustering Performance Analysis

Kazmi

2020

IJIKM

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Aim/Purpose: The clustering techniques are normally considered to determine the significant and meaningful subclasses purposed in datasets. It is an unsupervised type of Machine Learning (ML) where the objective is to form groups from objects based on their similarity and used to determine the implicit relationships between the different features of the data. Cluster Analysis is considered a significant problem area in data exploration when dealing with arbitrary shape problems in different datasets. Clustering on large data sets has the following challenges: (1) clusters with arbitrary shapes; (2) less knowledge discovery process to decide the possible input features; (3) scalability for large data sizes. Density-based clustering has been known as a dominant method for determining the arbitrary-shape clusters. Background: Existing density-based clustering methods commonly cited in the literature have been examined in terms of their behavior with data sets that contain nested clusters of varying density. The existing methods are not enough or ideal for such data sets, because they typically partition the data into clusters that cannot be nested. Methodology: A density-based approach on traditional center-based clustering is introduced that assigns a weight to each cluster. The weights are then utilized in calculating the distances from data vectors to centroids by multiplying the distance by the centroid weight. Contribution: In this paper, we have examined different density-based clustering methods for data sets with nested clusters of varying density. Two such data sets were used to evaluate some of the commonly cited algorithms found in the literature. Nested clusters were found to be challenging for the existing algorithms. In utmost cases, the targeted algorithms either did not detect the largest clusters or simply divided large clusters into non-overlapping regions. But, it may be possible to detect all clusters by doing multiple runs of the algorithm with different inputs and then combining the results. This work considered three challenges of clustering methods. Findings: As a result, a center with a low weight will attract objects from further away than a centroid with higher weight. This allows dense clusters inside larger clusters to be recognized. The methods are tested experimentally using the K-means, DBSCAN, TURN*, and IDCUP algorithms. The experimental results with different data sets showed that IDCUP is more robust and produces better clusters than DBSCAN, TURN*, and K-means. Finally, we compare K-means, DBSCAN, TURN*, and to deal with arbitrary shapes problems at different datasets. IDCUP shows better scalability compared to TURN*. Future Research: As future recommendations of this research, we are concerned with the exploration of further available challenges of the knowledge discovery process in clustering along with complex data sets with more time. A hybrid approach based on density-based and model-based clustering algorithms needs to compare to achieve maximum performance accuracy and avoid the arbitrary shapes related problems including optimization. It is anticipated that the comparable kind of the future suggested process will attain improved performance with analogous precision in identification of clustering shapes.

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Section: Parameter Reductionmentioning

confidence: 99%

IDCUP Algorithm to Classifying Arbitrary Shapes and Densities for Center-based Clustering Performance Analysis

Kazmi

2020

IJIKM

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Research on Thunderstorm Identification Based on Discrete Wavelet Transform

Li¹,

Xu²,

Han³

et al. 2022

Intelligent Automation &Amp; Soft Computing

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Lightning has been one of the most talked-about natural disasters worldwide in recent years, as it poses a great threat to all industries and can cause huge economic losses. Thunderstorms are often accompanied by natural phenomena such as lightning strikes and lightning, and many scholars have studied deeply the regulations of thunderstorm generation, movement and dissipation to reduce the risk of lightning damage. Most of the current methods for studying thunderstorms focus on using more complex algorithms based on radar or lightning data, which increases the computational burden and reduces the computational efficiency to some extent. This paper proposes a raster-based DWT (discrete wavelet transform) method for thunderstorm identification, this method uses DWT, CFSFD (clustering algorithm for fast search and finding density peaks) algorithm and ADTD (active divectory topology diagrammer) lightning location data for thunderstorm identification. The advantage of this method is that it supports different spatial resolutions and can identify any shape and number of thunderstorms at the same time and in the same area. It is effective in eliminating some of cluttered, scattered lightning data and extracting dense areas of thunderstorms. Furthermore, the method has a time complexity of O(n), and the computational efficiency is significantly better than the current TITAN (thunderstorm identification, tracking, analysis, and nowcasting) algorithm, which provides a good basis for subsequent extrapolation studies of thunderstorms.

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Prediction model for lightning nowcasting based on DBSCAN

Cited by 2 publications

References 0 publications

IDCUP Algorithm to Classifying Arbitrary Shapes and Densities for Center-based Clustering Performance Analysis

IDCUP Algorithm to Classifying Arbitrary Shapes and Densities for Center-based Clustering Performance Analysis

Research on Thunderstorm Identification Based on Discrete Wavelet Transform

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