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.
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Showing 1 to 20 of 27 for “"Fuzzy clustering"”.
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Robust techniques and applications in fuzzy clustering
… and outliers of least squares minimization based clustering techniques, such as Fuzzy c-Means (FCM) and its variants is addressed. In this work, two novel and robust clustering schemes are presented and analyzed in detail. They approach the problem of robustness from different perspectives. The …
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A study of type-2 fuzzy clustering
Fuzzy C-means (FCM) has been a prominent clustering algorithm for a long time. It was extended to a type-2 framework by the linguistic fuzzy C-means (LFCM) algorithm that operates on vectors of fuzzy numbers utilizing the extension principle, the decomposition theorem, and interval analyses. The …
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Fuzzy clustering with an application to scheduling
… is introduced in this thesis. A subtractive clustering based system identification method is developed to learn the scheduling decision mechanism from an existing schedule. It is utilized to build a fuzzy expert model. The existing schedule can be an optimal schedule developed using an …
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An enhanced progressive fuzzy clustering approach to pattern recognition
This thesis applies an enhanced progressive clustering approach, involving fuzzy clustering algorithms and fuzzy neural networks, to solve some practical problems of pattern recognition. A new fuzzy clustering framework, referred to as Cluster Prototype Centring by Membership (CPCM), has been …
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Online clustering with single-pass topology based fuzzy clustering algorithm
Online clustering is of significant interest for real-time data analysis. Generic offline clustering methods such as K-Means, C-Means and others are computationally expensive. The computational burden of these methods increases non-linearly with the size of the data set. In addition these methods …
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Exploratory Study of Fuzzy Clustering and Set-Distance Based Validation Indexes
This thesis is concerned with issues related to clustering. In particular, it addresses the con-vergence speed of fuzzy c-means family of algorithms and cluster validation. The fuzzy c-meansclustering algorithm and its objective function is studied along with a literature review of thespeed of …
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The Impact of Environmental Variables in Efficiency Analysis: A fuzzy clustering-DEA Approach
… two-stage framework is presented in this thesis. Fuzzy clustering is used in the first stage to suitably group the units with similar environments. In a subsequent stage, a relative efficiency analysis is performed on these groups. By approaching the problem in this manner the influence of …
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Solving Factorable Programs with Applications to Cluster Analysis, Risk Management, and Control Systems Design
… optimization problems, namely, the hard and fuzzy clustering problems, risk management problems, and problems arising in control systems. Under the umbrella of the broad RLT framework, the contributions of this dissertation focus on developing models and algorithms along with related …
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Robust Fuzzy Cluster Ensemble on Cancer Gene Expression Data
… research and biomedical applications. Many clustering algorithms have been applied to gene expression data to find patterns. Nonetheless, there are still a number of challenges for clustering gene expression data because of the specific characteristics of such data and the special …
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Application of Clustering Techniques to the Classification of Marine Phytoplankton
… is given with sections on Flow Cytometry, Clustering and so on. This includes a literature survey on research into fuzzy clustering algorithms, with a section specifically related to Flow Cytometry. Details are given about the data sets and the software used, and the clustering algorithms …
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Optimizing parameters in fuzzy k-means for clustering microarray data.
… 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 analysis of microarray data. However, the need of prior knowledge of the number …
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Classification of genes using clustering of chromatin state segmentations in human epigenomes
… A gene classification model was built using a k-fuzzy clustering approach of chromatin state features from a subset of training genes and then applied to a larger test set of genes. The models were found to be robust and show striking correspondence between training and test sets. 8 classes of …
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Activity segmentation with special emphasis on sit-to-stand analysis
… features to capture orientation of the body. Fuzzy clustering methods such as the Gustafson vessel algorithm are also investigated. The proposed algorithms were tested on 9 subjects with ages ranging from 18 to 88. The classification results were the best for the vowel height with the ellipse …
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Adaptive learning for event modeling and pattern classification
… this framework, a wavelet-based hierarchical fuzzy clustering approach which integrates several advanced technologies and overcomes the disadvantages of traditional clustering algorithms is developed to make the implementation of the system effective and computationally efficient. In another …
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Fuzzy Logic Approaches to Modelling, Identification and Control of Non-Linear Systems
… of control strategies and designing fuzzy controllers for uncertain systems using fuzzy logic techniques assisted by other conventional methods. The application of the proposed approaches are tested and evaluated on an underwater vehicle, where limited knowledge of the vehicle's …
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Assessment of Future Impact of Climate Change on Structural Protections and Hydrological Extremes – Floods
… climate classification with C-means clustering into the SDC2R2 model to capture the non-linear relationships among climate variables. Consequently, the developed model integrated fuzzy clustering along with Volterra series realization, principal components and ridge regression. The …
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Multitemporal mapping of burned areas in mixed landscapes in eastern Zambia
… areas uses multitemporal image analysis with a fuzzy clustering algorithm to automatically select spectral-temporal signatures that are then used to classify the images to produce the desired spatio-temporal burned area information. Testing with Landsat data (30m resolution) in eastern Zambia …
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Semantic Frameworks for Document and Ontology Clustering
… efficient ways to identify related publications. Clustering, a technique used in many fields, is one way to facilitate this. Ontologies can also help in addressing the problem of finding related entities, including research publications. However, the development of new methods of clustering has …
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An Intelligent Soft-Computing Texture Classification System
… or used in a new way. Neural networks and fuzzy clustering were applied for classification, while genetic algorithms provide a means for self optimisation.<br/><br/>The concepts and methods have been used for a number of projects next to texture classification itself. This work presents …
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An analysis of changing dietary trends and the implications for global health.
… scrutinised by econometric convergence tests, clustering techniques and spatial analysis. Results indicate that countries with lower levels of initial calories tend to exhibit higher growth rates of calorie consumption. However, this process is not homogeneous across countries. Low-income …
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