Metadata-Version: 2.1
Name: krepresentatives
Version: 1.1.2
Summary: A package for k-Representatives and LSH-k-Representatives
Home-page: https://github.com/
Author: Toan Nguyen Mau and Van-Nam Huynh
Author-email: nmtoan91@jaist.ac.jp
License: UNKNOWN
Description: Python implementations of the k-Representatives and LSH-k-Representatives algorithms for clustering categorical data:
        
        Different from k-Modes algorithm, k-Representatives and LSH-k-Representatives define the "representatives" that keep the frequencies of all categorical values of the clusters.
        
        ## Installation:
        ### Using pip: 
        ```shell
        pip install krepresentatives
        ```
        
        ### Import the packages:
        ```shell
        import numpy as np
        from krepresentatives.kRepresentatives import kRepresentatives
        ```
        ### Generate a simple categorical dataset:
        
        ```shell
        X = np.array([[0,0],[0,1],[0,0],[1,1],[2,2],[2,3],[2,3]])
        y = np.array([0,0,0,0,1,1,1])
        ```
        
        ### k-Representatives: 
        
        ```shell
        kreps = kRepresentatives(X,y,n_init=5,n_clusters=2 ,verbose=3)
        kreps.fit_predict()
        ```
        
        ### Built-in evaluattion metrics:
        ```shell
        kreps.CalcScore()
        ```
        
        ### Out come:
        ```shell
        kRepresentatives Init 0
        Iter 0  Cost: 8.00  Move: 0  Num empty: 0  Timelapse: 0.00
        Iter 1  Cost: 4.83  Move: 0  Num empty: 0  Timelapse: 0.00
        Iter 2  Cost: 4.83  Move: 0  Num empty: 0  Timelapse: 0.00
        kRepresentatives Init 1
        Iter 0  Cost: 9.48  Move: 0  Num empty: 0  Timelapse: 0.00
        Iter 1  Cost: 6.50  Move: 1  Num empty: 0  Timelapse: 0.00
        Iter 2  Cost: 5.33  Move: 0  Num empty: 0  Timelapse: 0.00
        Iter 3  Cost: 5.33  Move: 0  Num empty: 0  Timelapse: 0.00
        kRepresentatives Init 2
        Iter 0  Cost: 9.08  Move: 0  Num empty: 0  Timelapse: 0.00
        Iter 1  Cost: 7.60  Move: 0  Num empty: 0  Timelapse: 0.00
        Iter 2  Cost: 7.60  Move: 0  Num empty: 0  Timelapse: 0.00
        kRepresentatives Init 3
        Iter 0  Cost: 9.31  Move: 0  Num empty: 0  Timelapse: 0.00
        Iter 1  Cost: 6.50  Move: 1  Num empty: 0  Timelapse: 0.00
        Iter 2  Cost: 5.33  Move: 0  Num empty: 0  Timelapse: 0.00
        Iter 3  Cost: 5.33  Move: 0  Num empty: 0  Timelapse: 0.00
        kRepresentatives Init 4
        Iter 0  Cost: 9.42  Move: 0  Num empty: 0  Timelapse: 0.00
        Iter 1  Cost: 7.60  Move: 0  Num empty: 0  Timelapse: 0.00
        Iter 2  Cost: 7.60  Move: 0  Num empty: 0  Timelapse: 0.00
        Score:  4.833333333333334  Time: 0.0015569399999999956
        Purity: 1.00 NMI: 1.00 ARI: 1.00 Sil:  0.52 Acc: 1.00 Recall: 1.00 Precision: 1.00
        ```
        
        
        ## 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: 
        
        ## Outputs:
        cluster_representatives: List of final representatives\
        labels_: Prediction labels\
        cost_: Final sum of squared distance from objects to their centroids\
        n_iter_: Number of iterations\
        epoch_costs_: Average time for an initialization
        ## LSH-k-Representatives: *To be updated*
        
        ## References:
        [1] San, Ohn Mar, Van-Nam Huynh, and Yoshiteru Nakamori. "An alternative extension of the k-means algorithm for clustering categorical data." International journal of applied mathematics and computer science 14 (2004): 241-247.
        [2] *To be updated*
        
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
