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.
You can find a simple hands-on code snippet to play with on the Amethix Blog
Enjoy the show!
References
[1] S. Fortunato, “Community detection in graphs”, Physics Reports, volume 486, issues 3-5, pages 75-174, February 2010.
[2] Z. Yang, et al., “A Comparative Analysis of Community Detection Algorithms on Artificial Networks”, Scientific Reports volume 6, Article number: 30750 (2016)
[3] S. Dongen, “A cluster algorithm for graphs”, Technical Report, CWI (Centre for Mathematics and Computer Science) Amsterdam, The Netherlands, 2000.
[4] A. J. Enright, et al., “An efficient algorithm for large-scale detection of protein families”, Nucleic Acids Research, volume 30, issue 7, pages 1575-1584, 2002.
Training neural networks faster without GPU [RB] (Ep. 77)
How to generate very large images with GANs (Ep. 76)
[RB] Complex video analysis made easy with Videoflow (Ep. 75)
[RB] Validate neural networks without data with Dr. Charles Martin (Ep. 74)
Waterfall or Agile? The best methodology for AI and machine learning (Ep. 72)
Training neural networks faster without GPU (Ep. 71)
Validate neural networks without data with Dr. Charles Martin (Ep. 70)
Complex video analysis made easy with Videoflow (Ep. 69)
Episode 68: AI and the future of banking with Chris Skinner [RB]
Episode 67: Classic Computer Science Problems in Python
Episode 66: More intelligent machines with self-supervised learning
Episode 65: AI knows biology. Or does it?
Episode 64: Get the best shot at NLP sentiment analysis
Episode 63: Financial time series and machine learning
Episode 62: AI and the future of banking with Chris Skinner
Episode 61: The 4 best use cases of entropy in machine learning
Episode 60: Predicting your mouse click (and a crash course in deeplearning)
Episode 59: How to fool a smart camera with deep learning
Episode 58: There is physics in deep learning!
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