Timeline for Finding user similarities within informal data sets
Current License: CC BY-SA 3.0
11 events
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| Apr 13, 2017 at 12:50 | history | edited | CommunityBot |
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| Jun 6, 2015 at 9:04 | comment | added | Brian Topping | Thanks @hostjc, those are good ideas. I think you're right that I need to think in terms of features that can be accessed by the clusterer, time to respond is a perfect example of a non-obvious feature that would have a lot of very interesting applications. | |
| Jun 5, 2015 at 5:10 | answer | added | Henrique Nader | timeline score: 1 | |
| May 29, 2015 at 21:30 | comment | added | hostjc | Suppose you find that some people exchanged emails a lot (say 100 email exchanges or above). Can these people be further divided into two groups according to certain features, such as the average time to reply an email (within three days or longer than that)? In addition to clustering, you can do supervised learning - search newspaper stories to find some people who cared about each other and other people who didn't care that much, then check the characteristics of emails from these two groups. | |
| May 29, 2015 at 20:50 | comment | added | hostjc | Interesting project. The free Udacity class - Intro to Machine Learning (udacity.com/course/intro-to-machine-learning--ud120) also explores Enron Corpus. To discover which authors might have affinity, maybe one can start with examining the number of emails between two people, assuming the number of emails would indicate that they cared about each other enough so they didn't avoid emailing each other as much as possible. What's your learning goal in this project? | |
| May 10, 2015 at 7:00 | history | edited | Brian Topping | CC BY-SA 3.0 |
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| May 10, 2015 at 6:55 | comment | added | Aleksandr Blekh | You are welcome. Always glad to help. | |
| May 10, 2015 at 6:53 | comment | added | Brian Topping | Thanks @AleksandrBlekh, I should have been more specific that I am looking for high level direction than low level implementation. Your comment helps a lot with that. I use Wikipedia a lot, but it never crossed my mind to look there on this subject. I'll update the question to better reflect what I am after and follow with new questions in the future. | |
| May 10, 2015 at 6:35 | comment | added | Aleksandr Blekh | I think that your question is too broad to expect comprehensive enough answers. I would recommend to research yourself major high-level ML techniques (Wikipedia set of relevant articles is a decent starting point) and then formulate more narrow question(s). Since you've mentioned NER, you might find helpful my related answers here and (linked within) here. | |
| May 9, 2015 at 5:47 | review | First posts | |||
| May 10, 2015 at 17:45 | |||||
| May 9, 2015 at 5:44 | history | asked | Brian Topping | CC BY-SA 3.0 |