This report applies **Market Basket Analysis (MBA)**, a data mining technique traditionally used in retail to study product co-occurrence in shopping carts, to uncover hidden patterns within successful team compositions in League of Legends Season 11. The core analytical concept treats each winning team composition as a single transaction: the five champions played by the winning team in a match form a "basket," and individual champions are the "items" inside it. The goal is to identify sets of champions that statistically appear together most frequently in matches resulting in a victory. Connected to: https://rpubs.com/Marta_B/Lol_Champion_ClustersiDimension

This project applies unsupervised learning to a dataset of League of Legends champion base statistics sourced from Kaggle (Cute Dango, League of Legends Champions dataset, available at kaggle.com/datasets/cutedango/league-of-legends-champions) to discover whether champions naturally cluster into distinct statistical archetypes, and which features drive those groupings. The workflow combines three complementary approaches: Hard clustering (K-Means, Hierarchical) to identify stable, discrete champion archetypes, Soft clustering (Fuzzy C-Means) to quantify champion hybridity, how strongly each champion belongs to one archetype versus another, Dimensionality reduction (PCA, MDS, UMAP, t-SNE, SOM) to visualize the structure of the feature space and validate clustering results across multiple independent methods.