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 12 of 12 for “"c-means clustering"”.
-
Improving Attenuation Correction In Hybrid Positron Emission to mography
… algorithm based on a modified fuzzy c-means clustering method and gradient correlation was developed and validated to perform automatic registration in cardiac PET/CT data of different breathing protocols. A free- breathing MR protocol and post-process algorithm were developed to …
-
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 …
-
Epileptic Seizure Detection And Prediction From Electroencephalogram Using Neuro-Fuzzy Algorithms
… combination in spatial-temporal domain. Fuzzy c-means clustering technique was utilized for optimizing the membership functions for varying patterns in the feature domain. In addition, application of the adaptive neuro-fuzzy inference system (ANFIS) is presented for efficient classification of …
-
Urban computing using call detail records : mobility pattern mining, next-location prediction and location recommendation
… 3) and infer home and workplaces using K-means Clustering and Fuzzy C-means Clustering. The proposed method was implemented on MIT Reality Mining data, by which we demonstrate that with inference rates of 56% and 82%, the method can improve 79% and 34% in accuracy respectively in home and …
-
Neighbourhood profiling and classification for community safety
… final partition was created using the fuzzy c-means clustering technique, but alternative techniques were also employed and levels of agreement between the different results were measured. The design process also involved measuring the ability of different partitions to discriminate …
-
Flow pattern mapping of horizontal evaporating refrigerant flow based on capacitive void fraction measurements
… in combination with the use of the fuzzy c-means clustering algorithm. The clustering in the selected feature space, groups the data points in clearly separable areas in a flow pattern map. Applying the technique to the HFC datasets, the slug flows could be easily separated from non-slug …
-
Construction of efficient indexes from Fuzzy Clusters: preliminary study
… machine learning, there is a method of so-called clustering analysis. This method identifies a partition (i.e. a collection of classes or clusters) over data corresponding to their density. The thesis generally hypothesizes that a result of clustering analysis strongly corresponds to the optimal …
-
Assessment of Future Impact of Climate Change on Structural Protections and Hydrological Extremes – Floods
… by incorporating 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 use of reflectance classification for chlorophyll algorithm application across multiple optical water types in South African coastal waters
… The OWTs are determined through stepwise fuzzy c-means clustering of a systematically expanding and modified database constructed from in situ, synthetic and regionally extracted Medium Resolution Imaging Spectrometer (MERIS) Rᵣₛ. A database division allows separate and more detailed clustering of …
-
Module-based Analysis of Biological Data for Network Inference and Biomarker Discovery
… data analysis. For the purpose of evaluating clustering algorithms from a biological point of view, we propose a figure of merit based on Kullback-Leibler divergence between cluster membership and known gene ontology attributes. Several benchmark expression-based gene clustering algorithms are …
-
Investigation into the creation of an ambient intelligent physiology measurement environment to facilitate modelling of the human wellbeing
… adjusts with it. HAOEFA uses the fuzzy c-means clustering methodology for extracting membership functions (MFs) before building its set of fuzzy rules. These MFs together with the rules base constitute a major part of the proposed system. It has the ability to learn and model the …
-
Channel Probing for an Indoor Wireless Communications Channel
… discussed along with possible solutions. Four clustering methods are compared and their relative strengths and weaknesses are pointed out. The effects that errors in the clustering process have on parameter estimation and model performance are also simulated.