Data Mining (DM GEI - FIB)

1 Topics

1.1 Data Sciences Tools

Data Sciences Tools

1.2 Data preparation

Preprocessing - EDA

Preprocessing - Outliers detection

Preprocessing - Missing Data Treatment

Preprocessing - MIMMI

1.3 Visualization and Dimensionality reduction

Principal Components Analysis (ACP)

Simple Correspondence Analysis (ACS)

Multiple Correspondence Analysis (ACM)

Factorial Analysis with Mixed Data (FAMD)

1.4 Clustering

Métodos de Clustering

Profiling

1.5 Predictive Models

Lineal Regresion

Logistic Regresion

1.6 Asociation Rules

Asociation Rules

1.7 Decisions Trees, Bagging and Ensamble Methods

Decisions Trees

Random Forest

Bagging Methods

1.8 Discriminant models and Non Parametric Discriminant

Naives Bayes y k-Nearest Neighbour (KNN)

Linear Discriminant Analysis (LDA) y Cuadratic Discriminant Analysis (QDA)

1.9 Support Vector Machine (SVM)

Support Vector Machine (SVM)

1.10 Neuronal Networks (NN)

Artificial Neuronal Network (ANN)

Convolutional Neural Network (CNN)

2 Bases de datos

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