Table 3.
The feature analysis approaches provided in iLearnPlus
| Method | Algorithm (abbreviation) | Reference |
|---|---|---|
| Clustering | k-means (kmeans) | (85,86) |
| Mini-Batch K-means (MiniBatchKMeans) | (85,86) | |
| Gaussian mixture (GM) | (85,86) | |
| Agglomerative (Agglomerative) | (88) | |
| Spectral (Spectral) | (89) | |
| Markov clustering (MCL) | (87) | |
| Hierarchical clustering (hcluster) | (85,90) | |
| Affinity propagation clustering (APC) | (91) | |
| Mean shift (meanshift) | (92) | |
| DBSCAN (dbscan) | (93) | |
| Feature selection | Chi-square test (CHI2) | (38) |
| Information gain (IG) | (38,39) | |
| F-score value (FScore) | (94) | |
| Mutual information (MIC) | (95) | |
| Pearson's correlation coefficient (Pearson) | (96) | |
| Dimensionality reduction | Principal component analysis (PCA) | (97) |
| Latent dirichlet allocation (LDA) | (98) | |
| t-distributed stochastic neighbor embedding (t_SNE) | (99) | |
| Feature normalization | Z-Score (ZScore) | (15) |
| MinMax (MinMax) | (15) |