This article discusses the practical work on classifier algorithms like Decision Tree, Naïve Bayes, K-Nearest Neighbour and their consequences on the vote.arff dataset using Weka tool. It also explains the confusion matrix and the advantages of each algorithm. The article is relevant for students studying Data Mining and Visualization for Business Intelligence.

Part 2 - Practical and Report

Perform a data mining task to evaluate different classification algorithms on the vote.arff dataset in Weka. Write a report comparing the performance of Decision Tree, Naive Bayes, and k-Nearest Neighbour algorithms using confusion matrix and other performance metrics.

Discover classifier algorithms and confusion matrix with practical examples on vote.arff dataset using Weka tool.

Data Mining and Visualization for Business Intelligence - Desklib

Data Mining and Visualization for Business Intelligence

This report presents an analysis of a data mining evaluation of the performance of different classification algorithms. The three different classification algorithms include Decision Tree, Naive Bayes, and k-Nearest Neighbor (KNN). The report compares the performance of these algorithms and provides insights into their strengths and weaknesses.

Data Science and Big Data

Statistics and Probability

Data Mining and Visualization: Performance Comparison of Classification Algorithms

This article explores three different classification techniques in data mining - KNN, J48 and Naive Bayes. A comparative evaluation of these techniques has been performed in terms of accuracy and cost analysis. The study shows that decision tree (J48) performed better than KNN and Naive Bayes for the given dataset. The article provides a detailed explanation of each algorithm and their performance analysis.

Hartpury University and Hartpury College

Artificial Intelligence

Comparative Exploration of KNN, J48 and Lazy IBK Classifiers in Weka

This document explains the process of text classification using Naïve Bayes algorithm for Natural Language Processing. It covers the results, matrix vector, classification, Naïve Bayes classification, Support Vector Machine Classification, and conclusion. The document also includes Bibliography. 

Data Mining and Visualization for Business Intelligence

End of preview

Data Mining and Visualization: Performance Comparison of Classification Algorithms

Comparative Exploration of KNN, J48 and Lazy IBK Classifiers in Weka

Text Classification Using Naïve Bayes

Classification Performance Evaluation Tasks 2022

Assignment on Intelligent Systems for Analytics

Weka: Data Mining and Machine Learning Topic 2022

Data Mining and Visualization for Business Intelligence

End of preview

Data Mining and Visualization: Performance Comparison of Classification Algorithmslg...

Comparative Exploration of KNN, J48 and Lazy IBK Classifiers in Wekalg...

Text Classification Using Naïve Bayeslg...

Classification Performance Evaluation Tasks 2022lg...

Assignment on Intelligent Systems for Analyticslg...

Weka: Data Mining and Machine Learning Topic 2022lg...

Data Mining and Visualization: Performance Comparison of Classification Algorithms

Comparative Exploration of KNN, J48 and Lazy IBK Classifiers in Weka

Text Classification Using Naïve Bayes

Classification Performance Evaluation Tasks 2022

Assignment on Intelligent Systems for Analytics

Weka: Data Mining and Machine Learning Topic 2022