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aglomerative-hierarchical-clustering

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shreyansh-2003 / Clustering-Analysis-KMeans-vs-Agglomerative-Clustering-for-Large-Datasets

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This repo explores KMeans and Agglomerative Clustering effectiveness in simplifying large datasets for ML. Goals include dataset download, finding optimal clusters via Elbow and Silhouette methods, comparing clustering techniques, validating optimal clusters, tuning hyperparameters. Detailed explanations and analysis are provided.

hyperparameter-optimization clustering-algorithm kmeans-clustering hierarchical-clustering dendogram elbow-method silhouette-score comparitive-study aglomerative-hierarchical-clustering
  • Updated May 28, 2023
  • Jupyter Notebook

darian-catalin-cucer / android-sdui

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Server-driven UI refers to a design pattern in which the user interface is primarily controlled and rendered by a server, with the client serving as a display and interaction layer. This approach allows for a separation of concerns between the presentation and business logic, and can simplify client-side development.

javascript ruby kotlin java go html algorithm sdk js algorithms cpp box algorithm-challenges algorithms-and-data-structures sduino sdui sdui-desktop aglomerative-hierarchical-clustering
  • Updated Feb 12, 2023
  • Kotlin

cissagatto / HPML

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This repository hold all experiments conducted during my PhD (2019-2023). HPML means "Hybrid Partitions for Multi-Label Classification". SET-UP-1

ecc partitioning partitions multi-label-classification multi-label clus partitioning-label-space label-correlations label-space random-partitions multi-label-partitions community-detection-methods hybrid-partitions correlations-between-labels modeling-label-correlations exploring-label-correlations self-organized-maps aglomerative-hierarchical-clustering
  • Updated Sep 24, 2024

Pradyothsp / Customer_Segmentation_Clustering

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This project aims to use k-means and Agglomerative clustering to segment customers into different groups based on their characteristics and purchasing habits. The goal is to understand the similarities and differences between the customer segments, which can help inform marketing strategies and target specific groups of customers.

data-science clustering numpy pandas-dataframe tabular-data python3 pca-analysis feature-engineering unsupervised-learning kmeans-clustering elbow-method aglomerative-hierarchical-clustering
  • Updated Dec 22, 2022
  • Jupyter Notebook

DhanaShravya / Wine-clustering-and-classification-using-ML

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Wine Classification using Machine learning algorithms

clustering scikit-learn gaussian-mixture-models decision-tree-classifier kmeans-clustering svm-classifier gradient-boosting dbscan-clustering affinity-propagation multi-class-classification random-forest-classifier knn-classifier classification-models wine-classification adaboostclassifier birch-clustering aglomerative-hierarchical-clustering multi-class-roc
  • Updated Oct 10, 2023
  • Jupyter Notebook

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