Fast approximate minimum spanning tree based clustering algorithm

Jothi, R.; Mohanty, Sraban Kumar; Ojha, Aparajita

doi:10.1016/j.neucom.2017.07.038

Cited by 41 publications

(29 citation statements)

References 21 publications

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“…Indeed, these hybrid clustering methods can identify clusters of arbitrary shape by removing inconsistent edges and detect clusters of heterogeneous nature. MST-based clustering algorithm was proposed by Zahn [ 23 ]. Since then, some studies have been conducted to improve it (such as [ 5 , 23 , 24 ]).…”

Section: Methodsmentioning

confidence: 99%

“…Genetic algorithm is a good option to solve the local optimization of K -means and will give a proper initial cluster center [ 25 ]. Clustering based on MST is known for deriving disordered boundaries and outlier detection [ 23 ]. The MST-based clustering techniques have widely been used for efficient clustering [ 23 , 24 ].…”

Section: Introductionmentioning

confidence: 99%

“…Clustering based on MST is known for deriving disordered boundaries and outlier detection [ 23 ]. The MST-based clustering techniques have widely been used for efficient clustering [ 23 , 24 ]. The combination of partition-based and hierarchical clustering methods may also strengthen both approaches and discard their disadvantages [ 7 ].…”

Section: Introductionmentioning

confidence: 99%

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Does Determination of Initial Cluster Centroids Improve the Performance of K-Means Clustering Algorithm? Comparison of Three Hybrid Methods by Genetic Algorithm, Minimum Spanning Tree, and Hierarchical Clustering in an Applied Study

Pourahmad

Basirat

Rahimi

et al. 2020

Computational and Mathematical Methods in Medicine

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Random selection of initial centroids (centers) for clusters is a fundamental defect in K-means clustering algorithm as the algorithm’s performance depends on initial centroids and may end up in local optimizations. Various hybrid methods have been introduced to resolve this defect in K-means clustering algorithm. As regards, there are no comparative studies comparing these methods in various aspects, the present paper compared three hybrid methods with K-means clustering algorithm using concepts of genetic algorithm, minimum spanning tree, and hierarchical clustering method. Although these three hybrid methods have received more attention in previous researches, fewer studies have compared their results. Hence, seven quantitative datasets with different characteristics in terms of sample size, number of features, and number of different classes are utilized in present study. Eleven indices of external and internal evaluating index were also considered for comparing the methods. Data indicated that the hybrid methods resulted in higher convergence rate in obtaining the final solution than the ordinary K-means method. Furthermore, the hybrid method with hierarchical clustering algorithm converges to the optimal solution with less iteration than the other two hybrid methods. However, hybrid methods with minimal spanning trees and genetic algorithms may not always or often be more effective than the ordinary K-means method. Therefore, despite the computational complexity, these three hybrid methods have not led to much improvement in the K-means method. However, a simulation study is required to compare the methods and complete the conclusion.

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Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Does Determination of Initial Cluster Centroids Improve the Performance of K-Means Clustering Algorithm? Comparison of Three Hybrid Methods by Genetic Algorithm, Minimum Spanning Tree, and Hierarchical Clustering in an Applied Study

Pourahmad

Basirat

Rahimi

et al. 2020

Computational and Mathematical Methods in Medicine

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“…In addition, the OD matrix [7,8] and OD map [9] are useful tools for understanding the detailed patterns of OD flows.However, most enhanced OD visualization methods require predefined research regions, which inevitably reduces the accuracy of the analysis results. In recent years, clustering has become a hot topic because of its characteristic ability to identify spatial linkages without fixed boundaries and distill general rules from messy OD flows.Inspired by the deep insight of the similarity relationship between flows [10], the excellent performance of the optimum cut-based clustering [12], and the good scalability of the minimum spanning tree [13,14], we put forward a novel OD flow clustering method in this article, namely, Tree-based and Optimum Cut-based Origin-Destination Flow Clustering (TOCOFC), which is capable of extracting flow clusters with different spatial and temporal resolutions. In detail, we develop an OD flow similarity measurement method that includes a spatial version and a spatiotemporal version to quantify the similarity relationship between OD flows.…”

mentioning

confidence: 99%

“…Inspired by the deep insight of the similarity relationship between flows [10], the excellent performance of the optimum cut-based clustering [12], and the good scalability of the minimum spanning tree [13,14], we put forward a novel OD flow clustering method in this article, namely, Tree-based and Optimum Cut-based Origin-Destination Flow Clustering (TOCOFC), which is capable of extracting flow clusters with different spatial and temporal resolutions. In detail, we develop an OD flow similarity measurement method that includes a spatial version and a spatiotemporal version to quantify the similarity relationship between OD flows.…”

mentioning

confidence: 99%

Tree-Based and Optimum Cut-Based Origin-Destination Flow Clustering

Xiang

2019

IJGI

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Data about the movements of diverse objects, including human beings, animals, and commodities, are collected in growing amounts as location-aware technologies become pervasive. Clustering has become an increasingly important analytical tool for revealing travel patterns from large-scale movement datasets. Most existing methods for origin-destination (OD) flow clustering focus on the geographic properties of an OD flow but ignore the temporal information preserved in the OD flow, which reflects the dynamic changes in the travel patterns over time. In addition, most methods require some predetermined parameters as inputs and are difficult to adjust considering the changes in the users' demands. To overcome such limitations, we present a novel OD flow clustering method, namely, TOCOFC (Tree-based and Optimum Cut-based Origin-Destination Flow Clustering). A similarity measurement method is proposed to quantify the spatial similarity relationship between OD flows, and it can be extended to measure the spatiotemporal similarity between OD flows. By constructing a maximum spanning tree and splitting it into several unrelated parts, we effectively remove the noise in the flow data. Furthermore, a recursive two-way optimum cut-based method is utilized to partition the graph composed of OD flows into OD flow clusters. Moreover, a criterion called CSSC (Child tree/Child graph Self-Similarity Criterion) is formulated to determine if the clusters meet the output requirements. By modifying the parameters, TOCOFC can obtain clustering results for different time scales and spatial scales, which makes it possible to study movement patterns from a multiscale perspective. However, TOCOFC has the disadvantages of low efficiency and large memory consumption, and it is not conducive to quickly handling large-scale data. Compared with previous works, TOCOFC has a better clustering performance, which is reflected in the fact that TOCOFC can guarantee a balance between clusters and help to fully understand the corresponding patterns. Being able to perform the spatiotemporal clustering of OD flows is also a highlight of TOCOFC, which will help to capture the differences in the patterns at different times for a deeper analysis. Extensive experiments on both artificial spatial datasets and real-world spatiotemporal datasets have demonstrated the effectiveness and flexibility of TOCOFC. flow data make the visualization blurry, which drives the development of new approaches to more effectively discover the unknown information implicit in the data itself.A series of enhanced OD visualization methods [3][4][5][6][7][8][9][10][11] have been investigated to improve the ability to analyze OD flows from multiple research perspectives. With the purpose of eliminating the occlusion problem, Buchin [3] applied a spiral tree to the flow map, which makes the flow map very neat and clear by merging lines smoothly, although predefined study areas such as TAZs or administrative areas are needed. Selassie [4] presented a divided edge bundling method that bu...

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Identification of spatial defects in semiconductor manufacturing

Borgoni

Galimberti

Zappa

2021

Appl Stoch Models Bus & Ind

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In this article, we investigate the occurrence of defects in integrated circuit fabrication and show how spatial analysis can be effective in grasping and representing spatial regularities in defect patterns on the silicon supports, called wafers, used to produce microchips. Defects occurring on the wafer surface are the main cause of yield loss in the semiconductor industry; hence, to promptly detect an excess of defects and identify their spatial structure is crucial to the entire fabrication process. To address this hard-to-solve problem, this article proposes a concatenation of different methods, namely, a control chart, a clustering algorithm, and a graphical tool. First, a control chart based on the p-value of an appropriate test recognizes spatially structured defects on the wafer area.Then a clustering procedure grounded on the minimum spanning tree algorithm is adopted to identify those regions more prone to defect occurrences. Finally, alpha-shapes are employed to display their shape effectively. The suggested procedure proves to be extremely fast and effective, allowing its implementation in-line during the fabrication process. This provides a great advantage in modern microelectronics where items tend to be highly specialized and often produced in small lots. In particular, due to the Monte Carlo nature of the procedure, the control chart proposed hereafter does not require gold standard data to be set. This is particularly advantageous for small lot production, which is typically limited in time and does not permit to collect long time series of the charting statistics. K E Y W O R D Salpha-shapes, integrated circuits fabrication, minimum spanning tree algorithm, p-value control charts INTRODUCTIONSpatial statistics has long played a fundamental role in environmental, agriculture, and ecological studies as well as in economics, epidemiology, and other human sciences. Less usual are applications in industry and technology where the spatial dimension is often crucial to assess the quality of production processes. In this article, we investigate the spatial structure of defects occurring in integrated circuit fabrication. Integrated circuits are built through several different physical and chemical steps that are performed on thin silicon slices of a few 878

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Fast approximate minimum spanning tree based clustering algorithm

Cited by 41 publications

References 21 publications

Does Determination of Initial Cluster Centroids Improve the Performance of K-Means Clustering Algorithm? Comparison of Three Hybrid Methods by Genetic Algorithm, Minimum Spanning Tree, and Hierarchical Clustering in an Applied Study

Does Determination of Initial Cluster Centroids Improve the Performance of K-Means Clustering Algorithm? Comparison of Three Hybrid Methods by Genetic Algorithm, Minimum Spanning Tree, and Hierarchical Clustering in an Applied Study

Tree-Based and Optimum Cut-Based Origin-Destination Flow Clustering

Identification of spatial defects in semiconductor manufacturing

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