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You searched for subject:(Similarity Coefficient Method). Showing records 1 – 2 of 2 total matches.

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University of Wisconsin – Milwaukee

1. Alarjani, Ali Saeed. Introduction of Similarity Coefficient-based Clustering Algorithms to Global Petrochemical Facility Location.

Degree: PhD, Engineering, 2017, University of Wisconsin – Milwaukee

This research introduces a similarity coefficient-based clustering algorithm to determine the best location for a petrochemical manufacturing facility. The most global petrochemical critical attributes have been selected from relevant literature about manufacturing activities. These critical attributes have been quantified by real world numbers from the World Bank database and have been employed in the proposed model of the research. The model of the research uses the selected critical attributes data and clusters a hundred countries in similar groups according to their attractiveness level to the petrochemical facility location. The outcomes of the developed model are classifications that show the potential country for locating a petrochemical facility. Moreover, all countries have been ranked first according to their high potential cluster and within each cluster. These rankings also help to distinguish the candidate countries assigned to the same cluster. Advisors/Committee Members: Dr. Nidal H. Abu Zahra.

Subjects/Keywords: Critical Factors of Global Manufacturing; Facility Location Problem; Similarity Coefficient Method; Engineering; Industrial Engineering

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6th Edition):

Alarjani, A. S. (2017). Introduction of Similarity Coefficient-based Clustering Algorithms to Global Petrochemical Facility Location. (Doctoral Dissertation). University of Wisconsin – Milwaukee. Retrieved from https://dc.uwm.edu/etd/1569

Chicago Manual of Style (16th Edition):

Alarjani, Ali Saeed. “Introduction of Similarity Coefficient-based Clustering Algorithms to Global Petrochemical Facility Location.” 2017. Doctoral Dissertation, University of Wisconsin – Milwaukee. Accessed November 27, 2020. https://dc.uwm.edu/etd/1569.

MLA Handbook (7th Edition):

Alarjani, Ali Saeed. “Introduction of Similarity Coefficient-based Clustering Algorithms to Global Petrochemical Facility Location.” 2017. Web. 27 Nov 2020.

Vancouver:

Alarjani AS. Introduction of Similarity Coefficient-based Clustering Algorithms to Global Petrochemical Facility Location. [Internet] [Doctoral dissertation]. University of Wisconsin – Milwaukee; 2017. [cited 2020 Nov 27]. Available from: https://dc.uwm.edu/etd/1569.

Council of Science Editors:

Alarjani AS. Introduction of Similarity Coefficient-based Clustering Algorithms to Global Petrochemical Facility Location. [Doctoral Dissertation]. University of Wisconsin – Milwaukee; 2017. Available from: https://dc.uwm.edu/etd/1569


Queensland University of Technology

2. Wang, Danling. Multifractal characterisation and analysis of complex networks.

Degree: 2011, Queensland University of Technology

Complex networks have been studied extensively due to their relevance to many real-world systems such as the world-wide web, the internet, biological and social systems. During the past two decades, studies of such networks in different fields have produced many significant results concerning their structures, topological properties, and dynamics. Three well-known properties of complex networks are scale-free degree distribution, small-world effect and self-similarity. The search for additional meaningful properties and the relationships among these properties is an active area of current research. This thesis investigates a newer aspect of complex networks, namely their multifractality, which is an extension of the concept of selfsimilarity. The first part of the thesis aims to confirm that the study of properties of complex networks can be expanded to a wider field including more complex weighted networks. Those real networks that have been shown to possess the self-similarity property in the existing literature are all unweighted networks. We use the proteinprotein interaction (PPI) networks as a key example to show that their weighted networks inherit the self-similarity from the original unweighted networks. Firstly, we confirm that the random sequential box-covering algorithm is an effective tool to compute the fractal dimension of complex networks. This is demonstrated on the Homo sapiens and E. coli PPI networks as well as their skeletons. Our results verify that the fractal dimension of the skeleton is smaller than that of the original network due to the shortest distance between nodes is larger in the skeleton, hence for a fixed box-size more boxes will be needed to cover the skeleton. Then we adopt the iterative scoring method to generate weighted PPI networks of five species, namely Homo sapiens, E. coli, yeast, C. elegans and Arabidopsis Thaliana. By using the random sequential box-covering algorithm, we calculate the fractal dimensions for both the original unweighted PPI networks and the generated weighted networks. The results show that self-similarity is still present in generated weighted PPI networks. This implication will be useful for our treatment of the networks in the third part of the thesis. The second part of the thesis aims to explore the multifractal behavior of different complex networks. Fractals such as the Cantor set, the Koch curve and the Sierspinski gasket are homogeneous since these fractals consist of a geometrical figure which repeats on an ever-reduced scale. Fractal analysis is a useful method for their study. However, real-world fractals are not homogeneous; there is rarely an identical motif repeated on all scales. Their singularity may vary on different subsets; implying that these objects are multifractal. Multifractal analysis is a useful way to systematically characterize the spatial heterogeneity of both theoretical and experimental fractal patterns. However, the tools for multifractal analysis of objects in Euclidean space are not suitable for complex networks. In this…

Subjects/Keywords: complex networks, weighted networks, protein-protein interactions, fractal dimension, self-similarity, iterative scoring method, multifractal analysis, the generalized fractal dimension, scale-free networks, small-world networks, randomnetworks; gene networks, correlation coefficient, time series, fractional Brownian motion, Hurst index, binomial multifractal measure, measure representation of DNA sequence, degree distribution, resilience

Record DetailsSimilar RecordsGoogle PlusoneFacebookTwitterCiteULikeMendeleyreddit

APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6th Edition):

Wang, D. (2011). Multifractal characterisation and analysis of complex networks. (Thesis). Queensland University of Technology. Retrieved from https://eprints.qut.edu.au/48176/

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16th Edition):

Wang, Danling. “Multifractal characterisation and analysis of complex networks.” 2011. Thesis, Queensland University of Technology. Accessed November 27, 2020. https://eprints.qut.edu.au/48176/.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7th Edition):

Wang, Danling. “Multifractal characterisation and analysis of complex networks.” 2011. Web. 27 Nov 2020.

Vancouver:

Wang D. Multifractal characterisation and analysis of complex networks. [Internet] [Thesis]. Queensland University of Technology; 2011. [cited 2020 Nov 27]. Available from: https://eprints.qut.edu.au/48176/.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Wang D. Multifractal characterisation and analysis of complex networks. [Thesis]. Queensland University of Technology; 2011. Available from: https://eprints.qut.edu.au/48176/

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

.