A data clustering approach based on universal gravity rule

Bahrololoum, Abbas; Nezamabadi‐pour, Hossein; Saryazdi, Saeı̈d

doi:10.1016/j.engappai.2015.07.018

Cited by 26 publications

(12 citation statements)

References 42 publications

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“…Force-based clustering can be, at least, traced back to the literature [30] in 1977. Since then, many force-based methods have been proposed [23][24][25][26]31]. Like the proposed method, most force-based clustering methods assume that (i) the data points have masses, alike the particles in the physical world and (ii) the particle system undergoes a process of natural dynamic evolution, through which the data points gradually become clustered at last and then the members in each cluster are identified.…”

Section: Physically Inspired Clusteringmentioning

confidence: 99%

“…Proposed method. Physically inspired clustering also attracts wide interest, especially the force-based clustering [23][24][25][26], which regards each sample as a particle in the physical world and imitates the motion of the particles under certain forces. Because force-based clustering methods do not involve any assumption on the clusters, it is possible for them to handle different shapes of clusters.…”

Section: Introductionmentioning

confidence: 99%

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Nearest Descent, In-Tree, and Clustering

Qiu

2022

Mathematics

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Clustering aims at discovering the natural groupings in a dataset, prevalent in many disciplines that involve multivariate data analysis. In this paper, we propose a physically inspired graph-theoretical clustering method, which first makes the data points organized into an attractive graph, called In-Tree, via a physically inspired rule, called Nearest Descent (ND). The rule of ND works to select the nearest node in the descending direction of potential as the parent node of each node, which is fundamentally different from the classical Gradient Descent. The constructed In-Tree proves a very good candidate for clustering due to its particular features and properties. In the In-Tree, the original clustering problem is reduced to a problem of removing the inter-cluster edges from this graph. Pleasingly, those inter-cluster edges are usually so distinguishable that they can be easily determined by different automatic edge-cutting methods. We also propose a visualized strategy to validate the effectiveness of the automatic edge-cutting methods. The experimental results reveal that the proposed method is superior to the related clustering methods. The results also reveal the characteristics of different automatic cutting methods and the meaningfulness of the visualized strategy in increasing the reliability of the clustering results in practice.

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Section: Physically Inspired Clusteringmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Nearest Descent, In-Tree, and Clustering

Qiu

2022

Mathematics

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“…e clustering process is realized by using the differences among the LRFs of the data points close to the cluster centers and at the boundary of the clusters. Bahrololoum et al [26] proposed another approach that finds the best positions of the cluster centroids determined by employing the law of gravity. In the approach, the data points and cluster centroids are considered as fixed celestial objects and movable objects, respectively.…”

Section: Related Work Of Gravity-based Clusteringmentioning

confidence: 99%

A Novel Hierarchical Clustering Approach Based on Universal Gravitation

Zhang

She

2020

Mathematical Problems in Engineering

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The target of the clustering analysis is to group a set of data points into several clusters based on the similarity or distance. The similarity or distance is usually a scalar used in numerous traditional clustering algorithms. Nevertheless, a vector, such as data gravitational force, contains more information than a scalar and can be applied in clustering analysis to promote clustering performance. Therefore, this paper proposes a three-stage hierarchical clustering approach called GHC, which takes advantage of the vector characteristic of data gravitational force inspired by the law of universal gravitation. In the first stage, a sparse gravitational graph is constructed based on the top k data gravitations between each data point and its neighbors in the local region. Then the sparse graph is partitioned into many subgraphs by the gravitational influence coefficient. In the last stage, the satisfactory clustering result is obtained by merging these subgraphs iteratively by using a new linkage criterion. To demonstrate the performance of GHC algorithm, the experiments on synthetic and real-world data sets are conducted, and the results show that the GHC algorithm achieves better performance than the other existing clustering algorithms.

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“…In the data stream mining process, the data is assumed to be stationary at the beginning. How to solve the big data sample in the data stream is a more focused issue, so many researchers have proposed classification techniques to solve the problem of conceptual drift on data streams (Bahrololoum et al , 2015).…”

Section: Literature Reviewmentioning

confidence: 99%

A flexible planning methodology for product family assembly line based on improved NSGA_II

Zhao

et al. 2020

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Purpose The product family assembly line (PFAL) is a mixed model-assembly line, which is widely used in mass customization and intelligent manufacturing. The purpose of this paper is to study the problem of PFAL, a flexible (evolution) planning method to respond to product evolution for PFAL, to focus on product data analysis and evolution planning method. Design/methodology/approach The evolution balancing model for PFAL is established and an improved NSGA_II (INSGA_II) is proposed. From the perspective of data analysis, dynamic characteristics of PFAL are researched and analyzed. Especially the tasks, which stability is considered, can be divided into a platform and individual task. In INSGA_II algorithm, a new density selection and a decoding method based on sorting algorithms are proposed to compensate for the lack of traditional algorithms. Findings The effectiveness and feasibility of the method are validated by an example of PFAL evolution planning for a family of similar mechanical products. The optimized efficiency is significantly improved using INSGA_II proposed in this paper and the evolution planning model proposed has a stronger ability to respond to product evolution, which maximizes business performance over an effective period of time. Originality/value The assembly line designers and managers in discrete manufacturing companies can obtain an optimal solution for PFAL planning through the evolution planning model and INSGA-II proposed in this paper. Then, this planning model and optimization method have been successfully applied in the production of small wheel loaders.

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A data clustering approach based on universal gravity rule

Cited by 26 publications

References 42 publications

Nearest Descent, In-Tree, and Clustering

Nearest Descent, In-Tree, and Clustering

A Novel Hierarchical Clustering Approach Based on Universal Gravitation

A flexible planning methodology for product family assembly line based on improved NSGA_II

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