ijcruic / Multi-view-Clustering-of-Social-Based-Data

Python implementation of Multi-view Modularity Clustering (MVMC) for multi-view clustering of social-based data.

Multi-view-Clustering

Python implementations of multi-view clustering on social-based data.

This repository contains python implementations of algorihtms used in multi-view clustering of social-based data. The current algorithms implemented are as follows:

1. Cross View Influence Clustering (CVIC): An algorithm which works with intermediate and late paradigm clustering information

   • requires cluster labels and graphs for each view of the data
   • outputs the cluster labels for the objects and can also output cluster labels for clusters from the original view clusterings
   • suitbale for mid-sized data sets (up to ~10,000 objects)
   • references:
     • Cruickshank, I.J. Multi-view Clustering of Social-based Data. Ph.D. thesis, CMU-ISR-20-109. Carnegie Mellon University (7,2020)
     • Cruickshank I., Carley K. M. Analysis of Malware Communities Using Multi-Modal Features. IEEE Access. 8(1):77435-77448. May 2020. https://ieeexplore.ieee.org/document/9076000.

2. Multi-view Modularity Clustering (MVMC): An intermediate integration algorithm which works with view graphs of the data

   • requires graphs for each view of the data
   • outputs the cluster labels for the objects
   • suitable for large data (up to ~1,000,000 objects) with partially incomplete views
   • references:
     • Cruickshank, I.J. Multi-view Clustering of Social-based Data. Ph.D. thesis, CMU-ISR-20-109. Carnegie Mellon University (7,2020)

Each algorithm implementation is implemented as scikit-learn clustering module and can be used as such.