Spark library for generalized K-Means clustering. Supports general Bregman divergences. Suitable for clustering probabilistic data, time series data, high dimensional data, and very large data.
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Updated
Jan 19, 2024 - HTML
Spark library for generalized K-Means clustering. Supports general Bregman divergences. Suitable for clustering probabilistic data, time series data, high dimensional data, and very large data.
The goal of this project is to identify students at risk of dropping out the school
Color Quantization using K-Means(Machine learning)
Detecting Fake User Profiles using k-Means and Local Outlier Factor
Applied unsupervised learning techniques on demographic and spending data for a sample of German households.
Detects human activity by supervised and unsupervised methods same as SVM, Logistic Regression, Neural Networks, K-means, GMM and compares results
This course content includes that Classification(Supervised Method:),Clustering(Unsupervised Method),Association Analysis.
Recommendation of similar images to the given image using ResNet50, K-Means and cosine similarity.
Repository for the "Fundamentos del Aprendizaje Automático" subject
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Customer loyalty program for E-commerce. Feel free to access the report in the link below.
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Facilitating deep phenotyping by automating the screening of case studies & the comparison of patient similarities (through clustering). This can be used to get an understanding of the underlying pathophysiology for a rare genetic disorder.
Applying data mining techniques to a set of documents to determine meaningful relationships between them.
Application of PCA and K-means algorithms using R on FIFA19 data set.
Using fuzzy c-means and k-means to analyze customer personality data
Exploring meachine learning techniques and algorithms. Including clustering algorithms, perceptron and, more.
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