Data Mining: Introduction

Related lectures (32)

Page 1 of 4

Explores graph mining in social networks, covering data analysis and community detection.

Explores machine learning for predicting new product life cycles and the challenges of limited historical data.

Covers supervised and unsupervised learning techniques in machine learning, highlighting their applications in finance and environmental analysis.

Covers unsupervised learning, focusing on dimensionality reduction and clustering, explaining how it helps find patterns in data without labels.

Covers the theory and practice of clustering algorithms, including PCA, K-means, Fisher LDA, spectral clustering, and dimensionality reduction.

Introduces unsupervised learning through clustering with K-means and dimensionality reduction using PCA, along with practical examples.

Covers dimensionality reduction techniques like PCA and LDA, clustering methods, density estimation, and data representation.

Covers clustering, PCA, LDA, K-means, GMM, KDE, and Mean Shift algorithms for density estimation and clustering.

Covers Gaussian Naive Bayes, K-nearest neighbors, and hyperparameter tuning in machine learning.

Covers unsupervised learning with PCA and K-means for dimensionality reduction and data clustering.