Data Mining

16 lectures

Lecture notes and study resources for this subject.

01Lecture 01 · Data Mining Foundations and Core Concepts

02Lecture 02 · Data Mining Fundamentals

03Lecture 03 · Data and Data Preprocessing

04Lecture 04 · Data Preprocessing: Noise, Integration, Transformation, and Reduction

05Lecture 05 · Data Reduction, Discretization, and Classification

06Lecture 06 · Classification, Performance Measures, and Overfitting

07Lecture 07 · Decision Trees

08Lecture 08 · Rule-Based Classification

09Lecture 09 · K-Nearest Neighbor and Ensemble Methods

10Lecture 10 · Ensemble Algorithms

11Lecture 11 · Association Rule Mining

12Lecture 12 · Association Rule Mining: Apriori and FP-Growth

13Lecture 13 · Clustering and K-Means

14Lecture 14 · K-Means and Hierarchical Clustering

15Lecture 15 · Hierarchical Clustering and Density-Based Clustering

16Lecture 16 · Malware Detection: A Real-Life Data Mining Application

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