MultiCons: Multiple Consensus Clustering using Closed Patterns
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Updated
May 9, 2017
MultiCons: Multiple Consensus Clustering using Closed Patterns
Identification and characterization of the main groups of mall customers in order to gain a deeper understanding of their needs, preferences, and behaviors.
Using kmeans clustering in sklearn model to test a 2-dimension matrix,
Machine Learning foundation course offered by the University of Washington in Coursera
different methods of clustering of data
Outlier Detection Using Cluster Analysis
Data Science techniques applied to a purchase records dataset, in order to predict a given client's next purchase. Dataset was provided by NaranjaX. We used regression models to predict client's consumption, classification models to categorize clients based on monthly increase in consumption. Unsupervised learning models used for complementary e…
Bachelor Thesis: Application of Data Mining Methods for Customer Clustering. Segmenting customers of Shopify stores
DStream Clustering in Rust
It contains mini-projects as part of Spatial and Temporal Data mining Spring 2018 class.
Identifies potential customers in a general demographic data set from Germany, using clustering techniques.
This repository consists of 6 sections, detailing hands on Machine Learning Models: Regression, Classification, Clustering, AssocaitionRuleLearning, Deep Learning and Natural Language Processing Techniques
Performed Hierarchical, KMEANS, DBSCAN clustering on two datasets
Density-Based Clustering Validation
reproducing Luxburg 2006 paper "A Tutorial on Spectral Clustering", and performing tests on different datasets.
A repository of projects completed through the Springboard career track program.
Probabilistic Quantum Clustering
Notebook to enrich clustering going a little bit beyond Sklearn
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