Advanced Data Clustering Methods of Mining Web Documents

Sambasivam, Samuel; Theodosopoulos, Nick

doi:10.28945/2966

Cited by 13 publications

(4 citation statements)

References 7 publications

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“…The basic idea of the clustering algorithm analysis is to observe the close relationships among the samples, according to the numerical characteristics of the objects [45][46][47]. Once the distance between the samples has been defined, the samples with smaller distances are grouped together.…”

Section: Principle Of Clustering Algorithmmentioning

confidence: 99%

Quantitative Analysis of Global Terrorist Attacks Based on the Global Terrorism Database

Dong

et al. 2021

Sustainability

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Terrorist attacks have become a serious source of risk affecting the security of the international community. Using the Global Terrorism Database (GTD), in order to quantitatively study past terrorist attacks and their temporal and spatial evolution the analytic hierarchy process (AHP) was used to classify the degree of damage from terrorist attacks. The various factors influencing terrorist attacks were extracted and represented in three dimensions. Subsequently, using MATLAB for analysis and processing, the grading standards for terrorist attacks were classified into five levels according to the degree of hazard. Based on this grading standard, the top ten terrorist attacks with the highest degree of hazard in the past two decades were listed. Because the characteristics and habits of a terrorist or group exhibit a certain consistency, the K-means cluster analysis method was used to classify terrorists according to region, type of attack, type of target and type of weapon used by the terrorists. Several attacks that might have been committed by the same terrorist organization or individual at different times and in different locations were classified into one category, and the top five categories were selected according to the degree of sabotage inflicted by the organization or individual. Finally, the spatiotemporal evolution of terrorist attacks in the past three years was analyzed, considering the terrorist attack targets and key areas of terrorist attacks. The Middle East, Southeast Asia, Central Asia, and Africa were predicted to be the regions that will be most seriously affected by future global terrorist events. The terrorist attacks in Southeast Asia are expected to become more severe, and the scope of terrorist attacks in Africa is expected to widen. Civilians are the targets most at risk for terrorist attacks, and the corresponding risk index is considerably higher than it is for other targets. The results of this research can help individuals and the government to enable a better understanding of terrorism, improve awareness to prevent terrorism and enhance emergency management and rescue, and provide a solid and reliable basis and reference for joint counterterrorism in various countries and regions.

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Section: Principle Of Clustering Algorithmmentioning

confidence: 99%

Quantitative Analysis of Global Terrorist Attacks Based on the Global Terrorism Database

Dong

et al. 2021

Sustainability

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“…Spatial clustering is an important data analysis technology that searches and identifies a finite set of species or clusters and then describes the spatial data. Through clustering, dense and sparse regions can be identified and then global distribution patterns and interesting relationships among the data attributes can be determined [1,2]. Spatial cluster analyses, as a branch of statistics, have been studied for many years, and they have been widely used in many fields, including urban planning, ecological environment, public health, transportation systems, and market analysis.…”

Section: Introductionmentioning

confidence: 99%

Exploring Multidimensional Spatiotemporal Point Patterns Based on an Improved Affinity Propagation Algorithm

Cui

et al. 2019

IJERPH

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Affinity propagation (AP) is a clustering algorithm for point data used in image recognition that can be used to solve various problems, such as initial class representative point selection, large-scale sparse matrix calculations, and large-scale data with fewer parameter settings. However, the AP clustering algorithm does not consider spatiotemporal information and multiple thematic attributes simultaneously, which leads to poor performance in discovering patterns from massive spatiotemporal points (e.g., trajectory points). To resolve this issue, a multidimensional spatiotemporal affinity propagation (MDST-AP) algorithm is proposed in this study. First, the similarity of spatial and nonspatial attributes is measured in Gaussian kernel space instead of Euclidean space, which helps address the multidimensional linear inseparability problem. Then, the Davies-Bouldin (DB) index is applied to optimize the parameter value of the MDST-AP algorithm, which is applied to analyze road congestion in Beijing via taxi trajectories. Experiments on different datasets and algorithms indicated that the MDST-AP algorithm can process multidimensional spatiotemporal data points faster and more effectively.

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“…Therefore, the best method of cluster analysis depends on datasets and purposes of use. There is no universal clustering technology that can be widely applicable to the diverse structures presented by various datasets [1]. According to the accumulation rules of objects in clusters and the methods of applying these rules, clustering algorithms are divided into many types.…”

Section: Introductionmentioning

confidence: 99%

A Self-Adaptive Fuzzyc-Means Algorithm for Determining the Optimal Number of Clusters

Ren

Liu

Wang

et al. 2016

Computational Intelligence and Neuroscience

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For the shortcoming of fuzzy c-means algorithm (FCM) needing to know the number of clusters in advance, this paper proposed a new self-adaptive method to determine the optimal number of clusters. Firstly, a density-based algorithm was put forward. The algorithm, according to the characteristics of the dataset, automatically determined the possible maximum number of clusters instead of using the empirical rule n and obtained the optimal initial cluster centroids, improving the limitation of FCM that randomly selected cluster centroids lead the convergence result to the local minimum. Secondly, this paper, by introducing a penalty function, proposed a new fuzzy clustering validity index based on fuzzy compactness and separation, which ensured that when the number of clusters verged on that of objects in the dataset, the value of clustering validity index did not monotonically decrease and was close to zero, so that the optimal number of clusters lost robustness and decision function. Then, based on these studies, a self-adaptive FCM algorithm was put forward to estimate the optimal number of clusters by the iterative trial-and-error process. At last, experiments were done on the UCI, KDD Cup 1999, and synthetic datasets, which showed that the method not only effectively determined the optimal number of clusters, but also reduced the iteration of FCM with the stable clustering result.

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Advanced Data Clustering Methods of Mining Web Documents

Cited by 13 publications

References 7 publications

Quantitative Analysis of Global Terrorist Attacks Based on the Global Terrorism Database

Quantitative Analysis of Global Terrorist Attacks Based on the Global Terrorism Database

Exploring Multidimensional Spatiotemporal Point Patterns Based on an Improved Affinity Propagation Algorithm

A Self-Adaptive Fuzzyc-Means Algorithm for Determining the Optimal Number of Clusters

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