An implementation of the ID3 Decision tree algorithm.
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Updated
Jun 23, 2017 - C++
An implementation of the ID3 Decision tree algorithm.
K-means Clustering in CUDA with OpenGL Visualization
Visualize Kmeans clusters with OpenGL and Qt after application of t-SNE
Native C++ implementation of the k-means clustering algorithm
A Collection of independent projects
MNIST classication with KNN and NNs
A KMeans implemented in C++ with Python bindings and GPU acceleration
In this project, I have used two methods to detect lesions on the skin: K-Means Clustering and Connected Components Labeling. With these techniques, I was able to identify the primary region of the skin containing pigmented skin lesions. Additionally, I incorporated Run Length Encoding, a lossless compression method, to store the image data.
Multithread open source application for k-means clustering, support really big files (lineCount <= 1000000000, dimensionsCount <=1000, centroids count <=1000)
A C++ implementation of the 'K Means Clustering' algorithm
K-Means Algorithm implemented using sequential and parallel algorithms.
Parallel implementation of k-means with Cuda and OpenMP. Cuda version using a reduction
K-Mean C++ parallel implementation with OpenMP
K-Means clustering algorithm is used to determine if there is a leak in the pipe system.
Basic Image Processing exercises. (Trying to not use openCV implementation)
K-Means Clustering Algorithm with OpenMP 5.1 and C++ 17. The effective core utilization using the Intel Compiler 19.1 was 96% in 12 cores on a Windows 10 environment.
Let's get those centroids!
K-Means clustering algorithm implementation with OpenMP
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