K-Means and Fuzzy C-Means clustering on the Iris dataset and Sonar dataset

Fuzzy clustering is a form of clustering in which each data point can belong to more than one cluster. Clustering or cluster analysis involves assigning data points to clusters such that items in the same cluster are as similar as possible, while items belonging to different clusters are as dissimilar as possible

Segmentation of Brain tumor from noisy images using various Filters and Segmentation algorithms using Matlab.

Fuzzy c-means clustering algorithm implementation using Matlab.

Welcome to my Classical Learning Projects repository, where I showcase my work in the fields of supervised and unsupervised learning. Here, you'll find code and datasets for various projects, such as classification and clustering tasks, implemented using popular algorithms like decision trees, neural networks, and k-means.

DocClusterizer is a Java desktop application designed to analyze and cluster documents based on their content similarity. The application utilizes Lucene and Tika libraries to process various file extensions such as txt, pdf, docx, and pptx.

Fuzzy C-Means (FCM) is a clustering algorithm that assigns membership degrees to data points, allowing for soft assignment to clusters. It offers flexibility, robustness to noise, interpretability, scalability, and versatility in various domains such as pattern recognition and data mining.

Implementation of Fuzzy C-means algorithm using python. It is used for soft clustering purpose. Visualizing the algorithm step by step with the cluster plots at each step and also the final clusters.

Fuzzy C-Means