| ▼Npyclustering | PyClustering module that consists of general modules related to clustering, graph coloring, containers, oscillatory networks |
| ▼Ncluster | Pyclustering module for cluster analysis |
| Nagglomerative | Cluster analysis algorithm: agglomerative algorithm |
| Nbang | Cluster analysis algorithm: BANG |
| Nbirch | BIRCH (Balanced Iterative Reducing and Clustering using Hierarchies) cluster analysis algorithm |
| Nbsas | Cluster analysis algorithm: BSAS (Basic Sequential Algorithmic Scheme) |
| Ncenter_initializer | Collection of center initializers for algorithm that uses initial centers, for example, for K-Means or X-Means |
| Nclarans | Cluster analysis algorithm: CLARANS |
| Nclique | Cluster analysis algorithm: CLIQUE |
| Ncure | Cluster analysis algorithm: CURE |
| Ndbscan | Cluster analysis algorithm: DBSCAN |
| Nelbow | Elbow method to determine the optimal number of clusters for k-means clustering |
| Nema | Cluster analysis algorithm: Expectation-Maximization Algorithm for Gaussian Mixture Model |
| Nencoder | Module for representing clustering results |
| Nfcm | Cluster analysis algorithm: Fuzzy C-Means |
| Nga | Cluster analysis algorithm: Genetic clustering algorithm (GA) |
| Ngenerator | Cluster generator |
| Ngmeans | The module contains G-Means algorithm and other related services |
| Nhsyncnet | Cluster analysis algorithm: Hierarchical Sync (HSyncNet) |
| Nkmeans | The module contains K-Means algorithm and other related services |
| Nkmedians | Cluster analysis algorithm: K-Medians |
| Nkmedoids | Cluster analysis algorithm: K-Medoids |
| Nmbsas | Cluster analysis algorithm: MBSAS (Modified Basic Sequential Algorithmic Scheme) |
| Noptics | Cluster analysis algorithm: OPTICS (Ordering Points To Identify Clustering Structure) |
| Nrock | Cluster analysis algorithm: ROCK |
| Nsilhouette | Silhouette - method of interpretation and validation of consistency |
| Nsomsc | Cluster analysis algorithm: SOM-SC (Self-Organized Feature Map for Simple Clustering) |
| Nsyncnet | Cluster analysis algorithm: Sync |
| Nsyncsom | Cluster analysis algorithm: SYNC-SOM |
| Nttsas | Cluster analysis algorithm: TTSAS (Two-Threshold Sequential Algorithmic Scheme) |
| Nxmeans | Cluster analysis algorithm: X-Means |
| ▼Ncontainer | Pyclustering module of data structures (containers) |
| Ncftree | Data Structure: CF-Tree |
| Nkdtree | Data Structure: KD-Tree |
| ▼Ngcolor | Pyclustering module for graph coloring algorithms |
| Ndsatur | Graph coloring algorithm: DSATUR |
| Nhysteresis | Graph coloring algorithm: Algorithm based on Hysteresis Oscillatory Network |
| Nsync | Graph coloring algorithm based on Sync Oscillatory Network |
| ▼Nnnet | Neural and oscillatory network module |
| Ncnn | Chaotic Neural Network |
| Ndynamic_visualizer | Output dynamic visualizer |
| Nfsync | Oscillatory Neural Network based on Kuramoto model in frequency domain |
| Nhhn | Oscillatory Neural Network based on Hodgkin-Huxley Neuron Model |
| Nhysteresis | Neural Network: Hysteresis Oscillatory Network |
| Nlegion | Neural Network: Local Excitatory Global Inhibitory Oscillatory Network (LEGION) |
| Npcnn | Neural Network: Pulse Coupled Neural Network |
| Nsom | Neural Network: Self-Organized Feature Map |
| Nsync | Neural Network: Oscillatory Neural Network based on Kuramoto model |
| Nsyncpr | Phase oscillatory network for patten recognition based on modified Kuramoto model |
| Nsyncsegm | Double-layer oscillatory network with phase oscillator for image segmentation |
| Nsamples | Pyclustering module for samples |
| ▼Nutils | Utils that are used by modules of pyclustering |
| Ncolor | Colors used by pyclustering library for visualization |
| Ngraph | Graph representation (uses format GRPR) |
| Nmetric | Module provides various distance metrics - abstraction of the notion of distance in a metric space |
| Nsampling | Module provides various random sampling algorithms |
1.8.13