Search Results - (( data segmentation means algorithm ) OR ( java application learning algorithm ))
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Customer segmentation on clustering algorithms
Published 2023“…Then, k-means, DBSCAN, and GMM clustering algorithms are applied to segment customers based on their buying behaviour. …”
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Final Year Project / Dissertation / Thesis -
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Segmentation of MRI brain images using statistical approaches
Published 2011“…Noise is one of the obstacles for brain MRI segmentation. The non-Local means (NL-means) algorithm is a state-of-the art neighbourhood-based noisereduction method which is time-consuming and its accuracy can be improved. …”
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Thesis -
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Image clustering comparison of two color segmentation techniques
Published 2010“…This project proposed a two color segmentation techniques such as K-means and Fuzzy C-means clustering algorithm that are accurately segment the desired images, which have the same color as the pre-selected pixels with background subtraction. …”
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Thesis -
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K-Means Clustering Approach for Intelligent Customer Segmentation Using Customer Purchase Behavior Data
Published 2022“…In order to process the collected data and segment the customers, an learning algorithm is used which is known as K-Means clustering. …”
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Article -
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Customer segmentation using clustering techniques / Mohamad Amir Salihin Mustafa
Published 2024“…This study explores clustering techniques for customer segmentation, focusing on the K-Means algorithm in particular, and uses a dataset that was obtained from the customer data of an international supermarket company. …”
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Thesis -
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Liver segmentation on CT images using random walkers and fuzzy c-means for treatment planning and monitoring of tumors in liver cancer patients
Published 2017“…In the case of liver tumor segmentation, with a mean overlap error of 19.6% and mean absolute relative volume difference of 11.2%. …”
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Thesis -
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Image Segmentation using Enhanced K-means Clustering with Hybrid Image Filtering for Acute Leukemia Blood Cells Microscopic Images
Published 2024thesis::master thesis -
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Individual-tree segmentation and extraction based on LiDAR point cloud data
Published 2024“…Utilizing LiDAR point cloud data and ground-measured data from 30 plots, we examined the sensitivity of individual tree segmentation to key parameters by varying the grid values of the point cloud distance discriminant clustering algorithm and adjusting the canopy height resolution (CHR) of the watershed algorithm. …”
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Article -
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Automatic liver tumor segmentation on computed tomography for patient treatment planning and monitoring
Published 2016“…The proposed framework is fully automatic requiring no user interaction. The proposed segmentation evaluated on real-world clinical data from patients is based on a hybrid method integrating cuckoo optimization and fuzzy c-means algorithm with random walkers algorithm. …”
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Weighted subsethood segmented fuzzy time series for moving holiday electricity load demand forecasting
Published 2020“…This paper modifies the classical fuzzy time series (FTS) algorithm by applying weighted subsethood based algorithm (WSBA) in FTS algorithm using segmented electricity load demand time series data. …”
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Framework for stream clustering of trajectories based on temporal micro clustering technique
Published 2018“…The temporal micro cluster data structure is proposed in CC_TRS algorithm to store the summarized information for each group of similar segments. …”
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Thesis -
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Segmentation of MRI Prostate Images using Gaussian Mixture Models (GMMs)
Published 2016“…This method is also compared to K-Means algorithm to evaluate its performance with synthetic image and also clinical MRI image.…”
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Final Year Project -
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MRI segmentation of medical images using FCM with initialized class centers via genetic algorithm
Published 2008“…Fuzzy C-Mean (FCM) is one of the most popular Medical image clustering methods. …”
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Conference or Workshop Item -
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Customer profiling using K-means clustering method / Nik Asyraniasna Nik Mohd Asri
Published 2024“…Through the analysis of various customer data sets, such as people, products, promotion, place, the K-means algorithm can detect clusters that correspond to consistent client groups. …”
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Thesis -
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Segmentation Assisted Object Distinction For Direct Volume Rendering
Published 2013“…A set of image processing techniques are creatively employed in the design of K-means based hybrid segmentation algorithm.…”
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Spectral texture segmentation of Magnetic Resonance Imaging (MRI) brain images for glioma brain tumour detection / Rosniza Roslan
Published 2013“…Experiments conducted on 64 MRI images, of all sequences showed that texture energy is the best texture feature to be used in glioma segmentation. Fuzzy C-Means clustering algorithm is then used to segment texture energy features from 126 MRI brain images of all sequences. …”
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Thesis -
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Adaptive abdominal fat and liver segmentation of ct scan images for abdominal fat-fatty liver correlation
Published 2012“…Then the diffused fat in the segmented liver is evaluated by calculating the mean of liver attenuation (measured in Hounsfield Units) for the segmented liver. …”
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Thesis -
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