Metadata-Version: 2.1
Name: reval
Version: 0.0.1
Summary: Relative clustering validation to select best number of clusters
Home-page: https://github.com/IIT-LAND/reval_clustering
Author: Isotta Landi
Author-email: isotta.landi@iit.it
License: UNKNOWN
Project-URL: Documentation, https://reval.readthedocs.io/en/latest/
Description: # `reval`: stability-based relative clustering validation method to determine the best number of clusters
        
        Determining the number of clusters that best partitions a dataset can be a challenging task because of 1) the lack of a 
        priori information within an unsupervised learning framework; and 2) the absence of a unique clustering validation 
        approach to evaluate clustering solutions. Here we present `reval`: a Python package that leverages 
        stability-based relative clustering validation methods to determine best clustering solutions, as described in [1]. 
            
        Statistical software, both in R and Python, usually compute internal validation metrics that can be leveraged
        to select the number of clusters that best fit the data and open-source software solutions that easily implement 
        relative clustering techniques are lacking. The advantage of a relative approach over internal validation methods 
        lies in the fact that internal metrics exploit characteristics of the data itself to produce a result, 
        whereas relative validation converts an unsupervised clustering algorithm into a supervised classification problem, 
        hence enabling generalizability and replicability of the results.
        
        ## Requirements
        
            python>=3.6
            
        ## Installing
        
        From github:
        
            git clone https://github.com/IIT-LAND/reval_clustering
            pip install -r requirements.txt
        
        In alternative, with PyPI:
        
            pip install reval
        
        ## Documentation
        
        Code documentation can be found [here](https://reval.readthedocs.io/en/latest/). Documents include Python code 
        descriptions, `reval` usage examples, 
        performance on benchmark datasets, and common issues that can be encountered related to a dataset number of features 
        and samples.
        
        ## Refrences
        
        [1] Lange, T., Roth, V., Braun, M. L., & Buhmann, J. M. (2004). Stability-based validation of clustering solutions. 
        *Neural computation*, 16(6), 1299-1323.
        
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
