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 88 for “"Number of clusters"”.
-
Minimizing the number of clusters in mobile packet radio networks
Thesis (M.S.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 1986.
-
Bicriterion Clustering and Selecting the Optimal Number of Clusters via Agreement Measure
… been important tools to address a broad range of problems in fields such as image analysis, genomics, and many other areas. Basically, these clustering problems can be simplified as two aspects. The first is to estimate the number of clusters. The second one is to allocate each observation to …
-
Methods of Determining the Number of Clusters in a Data Set and a New Clustering Criterion
… problem is to determine the best estimate of the number of clusters, which has a deterministic effect on the clustering results. However, a limitation in current applications is that no convincingly acceptable solution to the best-number-of-clusters problem is available due to high …
-
Clustering Analysis of Zernike Coefficients From High Order Aberration Patients
… fifteen Zernike coefficients using the method of clustering of linear regression models (CLM). EM algorithm is used to infer the maximum likelihood estimate of parameters for each cluster. Bayesian information criterion (BIC) combined with Bootstrapped maximum volume (BMV) criterion are used to …
-
Acoustic Emission Signal Classification for Gearbox Failure Detection
<p>The purpose of this research is to develop a methodology and technique to determine the optimal number of clusters in acoustic emission (AE) data obtained from a ground test stand of a rotating H-60 helicopter tail gearbox by using mathematical algorithms and visual inspection. Signs of fatigue …
-
Integration analysis of product architecture to support effective team co-location
… efforts are greatly facilitated through the use of integration analysis. Teams working on a product development project need to be brought together into clusters to address interactions between the functions or product elements they represent. This thesis presents a stochastic clustering …
-
Data mining and visualization : real time predictions and pattern discovery in hospital emergency rooms and immigration data
Data mining is a versatile and expanding field of study. We show the applications and uses of a variety of techniques in two very different realms: Emergency department (ED) length of stay prediction and visual analytics. For the ED, we investigate three data mining techniques to predict a …
-
Optimizing parameters in fuzzy k-means for clustering microarray data.
Rapid advances of microarray technologies are making it possible to analyze and manipulate large amounts of gene expression data. Clustering algorithms, such as hierarchical clustering, self-organizing maps, k-means clustering and fuzzy k-means clustering, have become important tools for expression …
-
An integer programming clustering approach with application to recommendation systems
… recommendation approach based on finding clusters of similar customers using integer programming model which is to find the minimal number of clusters subjected to several similarity measures. The proposed recommendation method is compared with collaborative filtering, and the experimental …
-
Clustering Profiles in Generalized Linear Mixed Models Settings Using Bayesian Nonparametric Statistics
… and longitudinal data in which the distribution of the response variable is a member of the exponential family. This thesis introduces a novel method for simultaneous clustering of such data and estimation of parameters of the underlying generalized linear mixed models. Generalized linear mixed …
-
Analyzing the dependence structure of microarray data: a copula–based approach
The main 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 …
-
AN APPROACH TO AUTOMATIC DETECTION of SUSPICIOUS INDIVIDUALS IN A CROWD
… mobile visual search. In many cases, the process of building a hierarchical tree uses k-means clustering followed by geometric verification. However, the number of clusters is not known in advance, and sometimes it is randomly generated. This may lead to a congested clustering which can cause …
-
Essays on Experimental Design
… methods using a k-means style estimator of our model and propose information criteria to jointly select the number of clusters for each latent variable. I also contribute to the theory of clustering with an over-specified number of clusters and derive new convergence rates for this …
-
Localized Feature Selection For Unsupervised Learning
<p>Clustering is the unsupervised classification of data objects into different groups (clusters) such that objects in one group are similar together and dissimilar from another group. Feature selection for unsupervised learning is a technique that chooses the best feature subset for clustering. In …
-
Testing Measurement Invariance in Multilevel Data with Unequal Cross-Level Factor Structures
<p>The test of measurement invariance (MI) investigates whether observed items measure a construct in the same way across different groups or over times. Examining MI is a prerequisite for multiple group comparisons in psychological tests (Schmitt & Kuljanin, 2008). With the prevalence of …
-
Clustering Analysis of Zernike Coefficients Through Quantile Regression
… criterion (BIC) combined with a measure of uncertainty are used to determine the number of clusters. A comparison of likelihoods between the unclustered and the clustered Zernike coefficients is implemented to determine the quantile at which population heterogeneity is the most …
-
A Distance-Based Clustering Framework for Categorical Time Series: A Case Study in Episodes of Care Healthcare Delivery System
… is a central issue in health economics. Episodes of Care (EoC) is a compensation structure that bundles payments for healthcare interventions that belong to a well-defined health event. Since the variation of clinical pathways can drive the cost of healthcare, this research uses sequences of …
-
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 …
-
Optimal clustering techniques for metagenomic sequencing data
… made it possible to determine the composition of bacterial microbiota of the human body. Clustering algorithms have been used to search for core microbiota types in the vagina, but results have been inconsistent, possibly due to methodological differences. We performed an extensive comparison …
-
External Support Vector Machine Clustering
… Vector Machine (SVM) clustering algorithm clusters data vectors with no a priori knowledge of each vector's class. The algorithm works by first running a binary SVM against a data set, with each vector in the set randomly labeled, until the SVM converges. It then relabels data points that …
Page 1 of 5