Clustering Uncertain Data Using Voronoi Diagrams and R-Tree Index

Kao, Ben; Lee, Sau Dan; Lee, Foris K F; Cheung, David W.; Ho, Wai-Shing

doi:10.1109/tkde.2010.82

Cited by 70 publications

(3 citation statements)

References 29 publications

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“…Motive by this, Mondo (raldine Del Mondo et al, 2010) propose an approach to characterize the spatial and temporal data using several graph. To better index the spatial-temporal data, some researches try to use spatial structure to facilitate continuous queries on the location of moving objects (Kao, Lee, Lee, Cheung, & Ho, 2010;Manolopoulos, Nanopoulos, Papadopoulos, & Theodoridis, 2003;SimonasˇSaltenis, Leutenegger, & Lopez, 1999). Ben (Kao et al, 2010) investigate the problem of clustering uncertain object whose locations are described by probability density functions, and introduce an R-tree index to organize the uncertain objects so as to reduce pruning overheads.…”

Section: Spatio-temporal Data Analysismentioning

confidence: 99%

Moving Objects Gathering Patterns Retrieving based on Spatio-Temporal Graph

Zhang

Liu

et al. 2016

International Journal of Web Services Research

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Moving objects gathering pattern represents a group events or incidents that involve congregation of moving objects, enabling the analysis of traffic system. However, effectively and efficiently discovering the specific gathering pattern turns to be a remaining challenging issue since the large number of moving objects will generate high volume of trajectory data. In order to address this issue, the authors propose a moving object gathering pattern retrieving method that aims to support the retrieving of gathering patterns based on spatio-temporal graph. In this method, firstly the authors use an improved R-tree based density clustering algorithm (RT-DBScan) to index the moving objects and collect clusters. Then, they maintain a spatio-temporal graph rather than storing the spatial coordinates to obtain the spatio-temporal changes in real time. Finally, a gathering retrieving algorithm is developed by searching the maximal complete graphs which meet the spatio-temporal constraints. To the best of their knowledge, effectiveness and efficiency of the proposed methods are outperformed other methods on both real and large trajectory data.

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Section: Spatio-temporal Data Analysismentioning

confidence: 99%

Moving Objects Gathering Patterns Retrieving based on Spatio-Temporal Graph

Zhang

Liu

et al. 2016

International Journal of Web Services Research

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“…However, a simple network can be very complex and difficult to train. Further, if the dimension of the input data is high, then the training process will consume a lot of time and the accuracy of classification will also vary with the increase of dimension (Kao et al, 2010) in the training data. Generally, the techniques used in the neural network systems will depend on the application of the system.…”

Section: Problem Definitionmentioning

confidence: 99%

An Overview of Research Challenges for Classification of Cardiotocogram Data

Sundar¹

2013

Journal of Computer Science

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Cardiotocography (CTG) is a simultaneous recording of Fetal Heart Rate (FHR) and Uterine Contractions (UC).The most common diagnostic techniques to evaluate maternal and fetal well-being during pregnancy and before delivery. By observing the Cardiotocography trace patterns doctors can understand the state of the fetus. There are several signal processing and computer programming based techniques for interpreting a typical Cardiotocography data. A model based CTG data classification system using a supervised Artificial Neural Network (ANN) which can classify the CTG data based on its training data. The performance neural network based classification model has been compared with the most commonly used unsupervised clustering methods Fuzzy C-mean and k-mean clustering. The arrived results show that the performance of the supervised machine learning based classification approach provided significant performance than other compared unsupervised clustering methods. The traditional clustering methods can identify the Normal CTG patterns; they were incapable of finding Suspicious and Pathologic patterns. The ANN based classifier was capable of identifying Normal, Suspicious and Pathologic condition, from the nature of CTG data with very good accuracy.

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“…Given a set of data points, a Voronoi diagram is a partition of the space into cells, where a cell corresponding to a given data point is a locus of all points of space closest to this data point. Voronoi tessellation is commonly used in various fields of natural and medical sciences (Okabe et al, 1992, 2000; Aurenhammer, 1993; Ebeling and Wiedenmann, 1993; Ramella et al, 2001; Dupanloup et al, 2002; Wieland et al, 2007; Bishnu and Bhattacherjee, 2009; Kao et al, 2010; Edla and Jana, 2011), and in geographic information systems to define the partition cell, or catchment areas containing individual sites by their influence (Okabe et al, 1992, 2000). …”

Section: Introductionmentioning

confidence: 99%

Disease Risk Assessment Using a Voronoi-Based Network Analysis of Genes and Variants Scores

Chen

Mukerjee²,

Dorfman³

et al. 2017

Front. Genet.

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Much effort has been devoted to assess disease risk based on large-scale protein-protein network and genotype-phenotype associations. However, the challenge of risk prediction for complex diseases remains unaddressed. Here, we propose a framework to quantify the risk based on a Voronoi tessellation network analysis, taking into account the disease association scores of both genes and variants. By integrating ClinVar, SNPnexus, and DISEASES databases, we introduce a gene-variant map that is based on the pairwise disease-associated gene-variant scores. This map is clustered using Voronoi tessellation and network analysis with a threshold obtained from fitting the background Voronoi cell density distribution. We define the relative risk of disease that is inferred from the scores of the data points within the related clusters on the gene-variant map. We identify autoimmune-associated clusters that may interact at the system-level. The proposed framework can be used to determine the clusters that are specific to a subtype or contribute to multiple subtypes of complex diseases.

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Clustering Uncertain Data Using Voronoi Diagrams and R-Tree Index

Cited by 70 publications

References 29 publications

Moving Objects Gathering Patterns Retrieving based on Spatio-Temporal Graph

Moving Objects Gathering Patterns Retrieving based on Spatio-Temporal Graph

An Overview of Research Challenges for Classification of Cardiotocogram Data

Disease Risk Assessment Using a Voronoi-Based Network Analysis of Genes and Variants Scores

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