Metadata-Version: 2.1
Name: fkcenters
Version: 1.0.3
Summary: A python package for Fuzzy-k-Centers algorithm
Home-page: https://github.com/nmtoan91/fkcenters
Author: nmtoan91
Author-email: toan_stt@yahoo.com
License: UNKNOWN
Description: Python implementations of the FuzzykCenters algorithms for fuzzy clustering categorical data:
        
        ## Installation:
        ### Using pip: 
        ```shell
        pip install fkcenters
        ```
        
        ### Import the packages:
        ```shell
        from FkCenters.FkCenters import FkCenters
        from FkCenters import TDef
        import numpy as np
        ```
        ### Generate a simple categorical dataset:
        
        ```shell
        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): 
        
        ```shell
        algo = FkCenters(X,y ,k=TDef.k, alpha=TDef.alpha)
        algo.SetupMeasure("Overlap")
        algo.DoCluster()
        algo.CalcScore()
        
        
        ```
        
        ### Built-in evaluattion metrics:
        ```shell
        algo.CalcFuzzyScore()
        ```
        
        ### Outcome:
        ```shell
        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 FHVâ†“:0.02 FSâ†“:-2000.00 XBâ†“:0.11 BHâ†“: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:
        T. N. Mau and V. -N. Huynh, ``Kernel-Based k-Representatives Algorithm for Fuzzy Clustering of Categorical Data," 2021 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE), 2021, pp. 1-6, doi: 10.1109/FUZZ45933.2021.9494597.
        
        
Platform: UNKNOWN
Classifier: Programming Language :: Python :: 3
Classifier: License :: OSI Approved :: MIT License
Classifier: Operating System :: OS Independent
Requires-Python: >=3.6
Description-Content-Type: text/markdown
