Spatial Cluster Analysis for Etiological Research and Identification of Socio-environmental Risk Factors

2011 ◽  
pp. 172-187
Author(s):  
Michael Emch ◽  
Mohammod Ali
Keyword(s):  
Environmental Risk ◽  
Diarrheal Disease ◽  
Spatial Cluster ◽  
Clustering Methods ◽  
Clustering Techniques ◽  
Household Level ◽  
Disease Clustering ◽  
Spatial Cluster Analysis ◽  
Etiologic Research ◽  
Environmental Causes

This chapter describes the use of disease clustering methods using diarrheal disease data from a rural area of Bangladesh for which the authors created a household-level GIS database. Understanding distributions of diseases in space and time can be useful for etiologic research and socio-environmental risk factor identification. Disease clustering techniques are not only useful as etiological research tools for chronic diseases but also for infectious diseases. The magnitude of clustering in different areas can assist with the generation of hypotheses about the underlying socio-environmental causes of the clusters. Once clusters are identified, studies can then focus on the socio-environmental characteristics of the areas where clusters are found.

SPATIAL CLUSTER ANALYSIS AND EMISSION DETECTION BY USING GEOINFORMATION TECHNOLOGY

2018 ◽  
Vol 5 (86) ◽  
pp. 25-35
Author(s):  
G.G. Rapakov ◽  
E.A. Lebedeva ◽  
V.A. Gorbunov ◽  
K.A. Abdalov ◽  
O.V. Mel'nichuk
Keyword(s):  
Cluster Analysis ◽  
Spatial Cluster ◽  
Spatial Cluster Analysis ◽  
Emission Detection

Spatial cluster analysis of population amyotrophic lateral sclerosis risk in Ireland

Neurology ◽  
2015 ◽  
Vol 84 (15) ◽  
pp. 1537-1544 ◽  
Author(s):  
J. Rooney ◽  
A. Vajda ◽  
M. Heverin ◽  
M. Elamin ◽  
A. Crampsie ◽  
...  
Keyword(s):  
Cluster Analysis ◽  
Amyotrophic Lateral Sclerosis ◽  
Spatial Cluster ◽  
Spatial Cluster Analysis ◽  
Lateral Sclerosis

scholarly journals Spectrogram Classification Using Dissimilarity Space

2020 ◽  
Vol 10 (12) ◽  
pp. 4176 ◽  
Author(s):  
Loris Nanni ◽  
Andrea Rigo ◽  
Alessandra Lumini ◽  
Sheryl Brahnam
Keyword(s):  
Ad Hoc ◽  
Classification Problem ◽  
Space Representation ◽  
Support Vector ◽  
Clustering Methods ◽  
Audio Classification ◽  
Classification Problems ◽  
Clustering Techniques ◽  
Vector Space Representation ◽  
Better Than

In this work, we combine a Siamese neural network and different clustering techniques to generate a dissimilarity space that is then used to train an SVM for automated animal audio classification. The animal audio datasets used are (i) birds and (ii) cat sounds, which are freely available. We exploit different clustering methods to reduce the spectrograms in the dataset to a number of centroids that are used to generate the dissimilarity space through the Siamese network. Once computed, we use the dissimilarity space to generate a vector space representation of each pattern, which is then fed into an support vector machine (SVM) to classify a spectrogram by its dissimilarity vector. Our study shows that the proposed approach based on dissimilarity space performs well on both classification problems without ad-hoc optimization of the clustering methods. Moreover, results show that the fusion of CNN-based approaches applied to the animal audio classification problem works better than the stand-alone CNNs.

scholarly journals A Study on the Behavior of Clustering Techniques for Modeling Travel Time in Road-Based Mass Transit Systems

Proceedings ◽  
2019 ◽  
Vol 31 (1) ◽  
pp. 18
Author(s):  
Cristóbal ◽  
Padrón ◽  
Quesada-Arencibia ◽  
Alayón ◽  
Blasio ◽  
...  
Keyword(s):  
Travel Time ◽  
Behavior Pattern ◽  
Large Data ◽  
Mass Transit ◽  
Clustering Methods ◽  
Knowledge Modeling ◽  
Activity Data ◽  
Transit Systems ◽  
Clustering Techniques ◽  
Key Factor

In road-based mass transit systems, the travel time is a key factor affecting quality of service. For this reason, to know the behavior of this time is a relevant challenge. Clustering methods are interesting tools for knowledge modeling because these are unsupervised techniques, allowing hidden behavior patterns in large data sets to be found. In this contribution, a study on the utility of different clustering techniques to obtain behavior pattern of travel time is presented. The study analyzed three clustering techniques: K-medoid, Diana, and Hclust, studying how two key factors of these techniques (distance metric and clusters number) affect the results obtained. The study was conducted using transport activity data provided by a public transport operator.

Mining qualitative patterns in spatial cluster analysis

2012 ◽  
Vol 39 (2) ◽  
pp. 1753-1762 ◽  
Author(s):  
Ickjai Lee ◽  
Yang Qu ◽  
Kyungmi Lee
Keyword(s):  
Cluster Analysis ◽  
Spatial Cluster ◽  
Spatial Cluster Analysis

A comprehensive review of fuzzy-based clustering techniques in wireless sensor networks

Sensor Review ◽  
2017 ◽  
Vol 37 (3) ◽  
pp. 289-304 ◽  
Author(s):  
Manjeet Singh ◽  
Surender Kumar Soni
Keyword(s):  
Cluster Head ◽  
Sensor Nodes ◽  
Tabular Form ◽  
Clustering Methods ◽  
Content Type ◽  
Clustering Techniques ◽  
Variables Selection ◽  
Comprehensive Survey ◽  
Decision Making Processes ◽  
Art Research

Purpose This paper aims to discuss a comprehensive survey on fuzzy-based clustering techniques. The determination of an appropriate sensor node as a cluster head straightforwardly affects a network’s lifetime. Clustering often possesses some uncertainties in determining suitable sensor nodes as a cluster head. Owing to various variables, selection of a suitable node as a cluster head is a perplexing decision. Fuzzy logic is capable of handling uncertainties and improving decision-making processes even with insufficient information. Then, state-of-the-art research in the field of clustering techniques has been reviewed. Design/methodology/approach The literature is presented in a tabular form with merits and limitations of each technique. Furthermore, the various techniques are compared graphically and classified in a tabular form and the flowcharts of important algorithms are presented with pseudocodes. Findings This paper comprehends the importance and distinction of different fuzzy-based clustering methods which are further supportive in designing more efficient clustering protocols. Originality/value This paper fulfills the need of a review paper in the field of fuzzy-based clustering techniques because no other paper has reviewed all the fuzzy-based clustering techniques. Furthermore, none of them has presented literature in a tabular form or presented flowcharts with pseudocodes of important techniques.

scholarly journals Spatial Cluster Analysis by the Bin-Packing Problem and DNA Computing Technique

2013 ◽  
Vol 2013 ◽  
pp. 1-8 ◽  
Author(s):  
Xiyu Liu ◽  
Jie Xue
Keyword(s):  
Cluster Analysis ◽  
Time Complexity ◽  
Dna Computing ◽  
Bin Packing ◽  
Packing Problem ◽  
Spatial Cluster ◽  
Computing Technique ◽  
Bin Packing Problem ◽  
Data Points ◽  
Spatial Cluster Analysis

Spatial cluster analysis is an important data mining task. Typical techniques include CLARANS, density- and gravity-based clustering, and other algorithms based on traditional von Neumann's computing architecture. The purpose of this paper is to propose a technique for spatial cluster analysis based on sticker systems of DNA computing. We will adopt the Bin-Packing Problem idea and then design algorithms of sticker programming. The proposed technique has a better time complexity. In the case when only the intracluster dissimilarity is taken into account, this time complexity is polynomial in the amount of data points, which reduces the NP-completeness nature of spatial cluster analysis. The new technique provides an alternative method for traditional cluster analysis.

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