Podcasts Technology Data Science at Home How to cluster tabular data with Markov Clustering (Ep. 73)

How to cluster tabular data with Markov Clustering (Ep. 73)

Published: Aug. 20, 2019, 10:40 p.m.

In this episode I explain how a community detection algorithm known as Markov clustering can be constructed by combining simple concepts like random walks, graphs, similarity matrix. Moreover, I highlight how one can build a similarity graph and then run a community detection algorithm on such graph to find clusters in tabular data.\nYou can find a simple hands-on code snippet to play with on the Amethix Blog\xa0\nEnjoy the show!\xa0\n\xa0\nReferences\n[1] S. Fortunato, \u201cCommunity detection in graphs\u201d, Physics Reports, volume 486, issues 3-5, pages 75-174, February 2010.\n[2] Z. Yang, et al., \u201cA Comparative Analysis of Community Detection Algorithms on Artificial Networks\u201d, Scientific Reports volume 6, Article number: 30750 (2016)\n[3] S. Dongen, \u201cA cluster algorithm for graphs\u201d, Technical Report, CWI (Centre for Mathematics and Computer Science) Amsterdam, The Netherlands, 2000.\n[4] A. J. Enright, et al., \u201cAn efficient algorithm for large-scale detection of protein families\u201d, Nucleic Acids Research, volume 30, issue 7, pages 1575-1584, 2002.