Global ETD Search
Search theses and dissertations gathered from participating repositories worldwide. Every result links back to the library that holds it. No account is needed.
Results
Showing 1 to 20 of 113 for “"Clustering Methods"”.
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Clustering Methods for Network Adjacency Data
Clustering analysis aims to detect the topological community-structure of networks (connected graphs with n vertices and m edges), and studies inherent relations behind partitions. In this thesis, we consider far-reaching model-free clustering algorithms including Girvan and Newman’s edge …
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Clustering Methods for Delineating Regions of Spatial Stationarity
… of this kind of data, this paper develops methods of grouping data based on the necessary conditions for spatial statistical analysis. The purpose of this paper is to examine and develop methods that can be used to delineate regions of stationarity. One of the major assumptions used in …
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Novel pharmacophore clustering methods for protein binding site comparison
… polypharmacology and immunology. While multiple methods have been proposed for comparing binding sites, they tend to focus on comparing very similar proteins and have only been developed for small specific datasets or very targeted applications. None of these methods make use of the powerful …
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Linguistic Diversity On Africa: Clustering Methods Application On Language Typology Data
… of languages in Africa can be characterized by clustering along two important structural divides: synthetic – analytic and tonal – non-tonal. Several methods, including latent class analysis and CFA models, hierarchical clustering, k-means family algorithms and CART modes, using feature networks …
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An outcome-based statistical framework to select and optimize molecular clustering methods for infectious diseases
… populations. These imply outbreaks, but the methods used to create them often require a threshold to qualify clusters (ie. 99% average pairwise sequence identity among cases). This project demonstrates a framework to observe the way that cluster-based outbreak detection responds to threshold …
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Improving the accuracy and diversity of feature extraction from online reviews using keyword embedding and two clustering methods
… for product specifications. Traditional methods including surveys and interviews are still widely used to solve this problem, but with the increase of online channels such as Twitter and YouTube, customer opinions that can be collected online have increased exponentially. This online data …
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Some Model-Based and Distance-Based Clustering Methods for Characterization of Regional Ecological Stressor-Response Patterns and Regional Environmental Quality Trends
We develop statistical methods for evaluation of regional variation of ecological stressor-response relationships, and regional variation in temporal profiles of water quality, for application to data from monitoring stations on bodies of water. To evaluate regional variation in regression …
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Waste collection & street-sweeping route optimization using a 2-stage cluster algorithm & heuristic approaches
… The following thesis proposes a novel 2-stage clustering approach, namely the Static and Dynamic Clustering, to divide a municipalities road network into several operational areas in which the routes can be assigned. A method of generating optimal routes within the respective operational areas …
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Statistical and algorithmic foundation of K-means clustering
Clustering is a widely deployed unsupervised learning tool. Given data in the Euclidean spoace, K-means clustering is one of the most commonly used clustering methods, which minimize the distance between each point to the centroid of its assigned cluster. Among the popular clustering methods, SDP …
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Analyzing the dependence structure of microarray data: a copula–based approach
… aim of this Ph.D. dissertation is the study of clustering dependent data by means of copula functions with particular emphasis on microarray data. Copula functions are a popular multivariate modeling tool in each field where the multivariate dependence is of great interest and their use in …
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Studies on Combining Sequence and Structure for Protein Classification
… structures and functions. A large scale protein clustering can provide a useful platform to identify such principles of protein evolution. Manual classification schemes accurately group homologous proteins, but they are slow and subjective. Automatic protein clustering methods are largely based …
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Patterns of heart attacks
… In this paper we utilize classification and clustering data mining methods concurrently to determine whether a patient is at risk for a future myocardial infarction. Specifically, we apply the algorithms to medical claims data from more than 47,000 members over five years to: 1) find …
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State by State : automated alignment and analysis of state statutes
… alignment algorithms, and address the issue of clustering evaluation when documents may belong to multiple clusters. We also explore pairwise alignment strategies and assess these in comparison to clustering methods.
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Constrained Clustering for Frequency Hopping Spread Spectrum Signal Separation
… frequency band. Traditional signal separation methods often require difficult to obtain hardware fingerprinting characteristics or approximate geo-location estimates. This work will consider the characteristics of FHSS signals that can be extracted directly from signal detection. From estimates …
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Criminal data analysis based on low rank sparse representation
FINDING effective clustering methods for a high dimensional dataset is challenging due to the curse of dimensionality. These challenges can usually make the most of basic common algorithms fail in highdimensional spaces from tackling problems such as large number of groups, and overlapping. Most …
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Learning Statistical and Geometric Models from Microarray Gene Expression Data
… develop innovative data modeling and analysis methods for extracting meaningful and specific information about disease mechanisms from microarray gene expression data. To provide a high-level overview of gene expression data for easy and insightful understanding of data structure, we propose a …
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Novel Techniques for Single-cell RNA Sequencing Data Imputation and Clustering
… the cellular heterogeneity within tumors, these methods have shed light on the mechanisms of tumor evolution, metastasis, and therapy resistance. Additionally, they have facilitated the identification of rare cell populations with specialized functions, such as stem cells and tissue-resident …
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Microarray time-series data clustering via gene expression profile alignment
Clustering gene expression data given In terms of time-series is a challenging problem that imposes its own particular constraints, namely, exchanging two or more time points is not possible as it would deliver quite different results and would lead to erroneous biological conclusions. In this …
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Unsupervised discovery and validation of affective engagement states using synchronized EEG and eye-tracking during digital learning
… of predefined emotion labels, unsupervised clustering methods were applied to identify latent engagement patterns directly from EEG features. To ensure robustness, non-overlapping parity analysis and hold-out validation were performed. Statistical tests were conducted to compare FAA and BA …
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Multivariate Outlier Mining Using Cluster Analysis: Case Study - National Health Interview Survey
… "outlyingness". There are several approaches and methods to detect anomalous data points in data. But no single method is perfect for every data set especially when the data dimension and volume is high. In this thesis, I review distance-based clustering methods for multivariate outlier mining and …
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