| Algorithm | Tab to Go | Select From |
|---|---|---|
| Decision Tree (J48) | Classify | Trees → J48 |
| Bayesian Classifier (Naive Bayes) | Classify | Bayes → NaiveBayes |
| SVM (SMO) | Classify | Functions → SMO |
| Random Forest | Classify | Trees → RandomForest |
| Adaboost (LogitBoost) | Classify | Meta → LogitBoost |
| Backpropagation (Multilayer Perceptron) | Classify | Functions → MultilayerPerceptron |
| K-Means Clustering | Cluster | Clusterer → SimpleKMeans |
| BIRCH Clustering | Cluster | Clusterer → BIRCH |
| DBSCAN Clustering | Cluster | Clusterer → DBSCAN |
| CLIQUE Clustering | Cluster | Clusterer → CLIQUE |
| Apriori (Association Rules) | Associate | Associate → Apriori |
| FP-Growth (Frequent Pattern Mining) | Associate | Associate → FP-Growth |
- The Classify tab is for classification tasks (e.g., J48, NaiveBayes, SMO).
- The Cluster tab is for clustering tasks (e.g., KMeans, DBSCAN, BIRCH).
- The Associate tab is for association rule mining and frequent pattern mining (e.g., Apriori, FP-Growth).
- For all classification and clustering tasks, dataset.csv is used. (a simple dataset with columns: ID, Age, Income, Student, CreditRating, BuysComputer).
- For Apriori and FP-Growth, new_dataset.csv dataset containing transactional data for association rule mining is used.
pip install numpy pandas (imp)