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How to cluster tabular data with Markov Clustering
Archived series ("Inactive feed" status)
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This feed was archived on November 26, 2019 01:33 (
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Manage episode 240205777 series 2362678
Content provided by Francesco Gadaleta. All podcast content including episodes, graphics, and podcast descriptions are uploaded and provided directly by Francesco Gadaleta or their podcast platform partner. If you believe someone is using your copyrighted work without your permission, you can follow the process outlined here https://player.fm/legal.
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.
60 episodes
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Archived series ("Inactive feed" status)
When?
This feed was archived on November 26, 2019 01:33 (
Why? Inactive feed status. Our servers were unable to retrieve a valid podcast feed for a sustained period.
What now? You might be able to find a more up-to-date version using the search function. This series will no longer be checked for updates. If you believe this to be in error, please check if the publisher's feed link below is valid and contact support to request the feed be restored or if you have any other concerns about this.
Manage episode 240205777 series 2362678
Content provided by Francesco Gadaleta. All podcast content including episodes, graphics, and podcast descriptions are uploaded and provided directly by Francesco Gadaleta or their podcast platform partner. If you believe someone is using your copyrighted work without your permission, you can follow the process outlined here https://player.fm/legal.
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.
60 episodes
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Similar to Data Science at Home
Linear Digressions is a podcast about machine learning and data science. Machine learning is being used to solve a ton of interesting problems, and to accomplish goals that were out of reach even a few short years ago.
…
continue reading
The O'Reilly Data Show Podcast explores the opportunities and techniques driving big data, data science, and AI.
…
continue reading
The Data Skeptic Podcast features interviews and discussion of topics related to data science, statistics, machine learning, artificial intelligence and the like, all from the perspective of applying critical thinking and the scientific method to evaluate the veracity of claims and efficacy of approaches.
…
continue reading
Talking Machines is your window into the world of machine learning. Your hosts, Katherine Gorman and Neil Lawrence, bring you clear conversations with experts in the field, insightful discussions of industry news, and useful answers to your questions. Machine learning is changing the questions we can ask of the world around us, here we explore how to ask the best questions and what to do with the answers. Hosted on Acast. See acast.com/privacy for more information.
…
continue reading
Brain fun for curious people.
…
continue reading
WIRED’s Gadget Lab podcast breaks down which gadgets, apps, and services you need to know about, and which ones you can move to the virtual trash bin. Learn how today’s tech shapes our lives—plus get your hosts’ personal recommendations at the end of each episode. Share your thoughts on WIRED Gadget Lab. As a token of our appreciation, you will be eligible to enter a prize drawing up to $1,000 after you complete the survey. https://selfserve.decipherinc.com/survey/selfserve/222b/76152?pin=1& ...
…
continue reading
Welcome to the Security Weekly Podcast Network, your all-in-one source for the latest in cybersecurity! This feed features a diverse lineup of shows, including Application Security Weekly, Business Security Weekly, Paul's Security Weekly, Enterprise Security Weekly, and Security Weekly News. Whether you're a cybersecurity professional, business leader, or tech enthusiast, we cover all angles of the cybersecurity landscape. Tune in for in-depth panel discussions, expert guest interviews, and ...
…
continue reading
Daily update on current cyber security threats
…
continue reading
Hungry for Technology Talk? Get All You Can Eat at the Android Buffet
…
continue reading
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