Crisp clustering algorithm for categorical data:

https://pypi.org/project/lshkrepresentatives/

Python implementations of the FuzzykCenters algorithms for fuzzy clustering categorical data:

Installation:

Using pip:

pip install fkcenters

Import the packages:

from FkCenters.FkCenters import FkCenters
from FkCenters import TDef
import numpy as np

Generate a simple categorical dataset:

X = np.array([[0,0,0],[0,1,1],[0,0,0],[1,0,1],[2,2,2],[2,3,2],[2,3,2]])
y = np.array([0,0,0,0,1,1,1])

LSHk-Representatives (Init):

algo = FkCenters(X,y ,k=TDef.k, alpha=TDef.alpha)
algo.SetupMeasure("Overlap")
algo.DoCluster()
algo.CalcScore()

Built-in evaluattion metrics:

algo.CalcFuzzyScore()

Outcome:

SKIP LOADING distMatrix because: True bd=None yellow
Saving Overlap to: saved_dist_matrices/json/Overlap_None.json
Purity: 1.00 NMI: 1.00 ARI: 1.00 Sil:  0.59 Acc: 1.00 Recall: 1.00 Precision: 1.00
Fuzzy scores PC:1.00 NPC:1.00 FHV↓:0.02 FS↓:-2000.00 XB↓:0.11 BH↓:0.06 BWS:-2000.00 FPC:3.50 SIL_R:0.70 FSIL:0.70 MPO:12.15 NPE:0.01 PE:0.01 PEB:0.01

Parameters:

X: Categorical dataset
y: Labels of object (for evaluation only)
n_init: Number of initializations
n_clusters: Number of target clusters
max_iter: Maximum iterations
verbose:
random_state:

If the variable MeasureManager.IS_LOAD_AUTO is set to "True": The DILCA will get the pre-caculated matrix

Outputs:

cluster_representatives: List of final representatives
labels_: Prediction labels
u: Fuzzy membership cost_: Final sum of squared distance from objects to their centroids
n_iter_: Number of iterations

References:

Mau, Toan Nguyen, and Van-Nam Huynh. "Kernel-Based k-Representatives Algorithm for Fuzzy Clustering of Categorical Data." 2021 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE). IEEE, 2021.