Data Mining with WEKA Training in Copenhagen, Denmark

This training introduces the fundamental concepts of data mining and guides participants in using Weka, a user-friendly machine learning tool. The course combines theory with hands-on exercises to help learners gain practical experience with classification, clustering, and association rule mining techniques.

Introduction to Data Mining and Weka

Introduction to Data Mining

• What is data mining
• Types of data mining tasks: classification, clustering, association
• Differences between data mining, machine learning, and AI
• Real-world applications of data mining

Getting Started with Weka

• Overview of Weka software
• Installation and interface walkthrough
• Loading datasets (ARFF and CSV formats)
• Exploring data: summary statistics and visualization

Classification in Weka

• Introduction to classification and supervised learning
• Using J48 decision tree and Naive Bayes algorithms
• Running classification tasks in Weka
• Understanding model output and confusion matrix

Model Evaluation

• Splitting datasets: percentage split and cross-validation
• Accuracy, precision, recall, and F1 score
• ROC curves and interpretation
• Comparing models using evaluation metrics

Advanced Techniques and Applications

Clustering in Weka

• Introduction to clustering and unsupervised learning
• K-Means clustering algorithm
• Running clustering analysis in Weka
• Interpreting clustering results and cluster visualization

Association Rule Mining

• Introduction to association rules
• Support, confidence, and lift explained
• Using Apriori algorithm in Weka
• Extracting and analyzing rules from datasets

Real-World Case Study

• Importing and cleaning a real-world dataset
• Choosing appropriate mining tasks
• Running multiple algorithms for comparison
• Drawing insights from output

Review and Assessment

• Recap of all techniques learned
• Group discussion: applications in participants' domains
• Mini quiz or practical assignment
• Q&A and feedback