[CSE 4255] Introduction to Data Mining and Warehousing Lab

Library and hand-made clustering algorithms are implemented in this project

Improve Text Categorization using k-Medoids Clustering Feature Selection

Segmenting with Mixed Type Data - A Case Study Using K-Medoids on Subscription Data

A fun side project to perform machine learning algorithms using plain java code.

Repository for Customer Segment Analysis using Python & Shiny App Dashboard

Julia implementation of unsupervised learning methods for time series datasets. It provides functionality for clustering and aggregating, detecting motifs, and quantifying similarity between time series datasets.

E-commerce customers automatic grouping by unsupervised ML/AI. Data from the Kaggle Olist dataset

Graph clustering project using Markov clustering algorithm, K-medoid algorithm, Spectral algorithm with GUI PyQt5

Calculating pairwise euclidean distance matrix for horizontally partitioned data in federated learning environment

Use unsupervised machine learning techniques to explore the Leukemia dataset by focusing more on dimensional reduction and clustering to find similarities between samples or how they are related to each other.

This is a capstone research project for my Certificate in Applied Data Science (CADS) at my undergraduate institution, Wesleyan University, on the topic of "Understanding the Variances in COVID-19 Pandemic Outcome - Excess Mortality - with Social, Cultural, and Environmental Factors", sponsored by Prof. Maryam Gooyabadi.

Comparing different clustering algorithms

statistical inference project with the task of clustering

Conducted a comprehensive clustering analysis to categorize beers based on features such as Astringency, Alcohol content, Bitterness, Sourness, and more. Utilized k-medoids and hierarchical agglomerative clustering algorithms to achieve this classification. Tech: Python (numpy, pandas, seaborn, matplotlib, sklearn, scipy)

Conducted a comprehensive clustering analysis to categorize beers based on features such as Astringency, Alcohol content, Bitterness, Sourness, and more. Utilized k-medoids and hierarchical agglomerative clustering algorithms to achieve this classification. Tech: Python (numpy, pandas, seaborn, matplotlib, sklearn, scipy)

Selection of the best centroid based clustering version with k-medoids and k-means