[ml] ml Digest, Vol 38, Issue 1

Mon Jan 21 12:17:51 UTC 2013

you can read that paper here:

http://www.bradblock.com/Top_10_algorithms_in_data_mining.pdf

and papers on the algorithms discussed as well as many more here:

http://www.bradblock.com/mll.html

if you need examples of how these algorithms have been used, i've applied almost all of them to some problem or another.

--- On Mon, 1/21/13, ml-request at lists.noisebridge.net <ml-request at lists.noisebridge.net> wrote:

> From: ml-request at lists.noisebridge.net <ml-request at lists.noisebridge.net>
> Subject: ml Digest, Vol 38, Issue 1
> To: ml at lists.noisebridge.net
> Date: Monday, January 21, 2013, 7:00 AM
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> Today's Topics:
> 
>    1. Fwd: [Noisebridge-discuss] I'm working
> on a series of
>       articles about algorithms - data mining
> experts sought asap
>       (Mike Schachter)
> 
> 
> ----------------------------------------------------------------------
> 
> Message: 1
> Date: Sun, 20 Jan 2013 10:35:44 -0800
> From: Mike Schachter <mschachter at eigenminds.com>
> To: ml <ml at lists.noisebridge.net>
> Subject: [ml] Fwd: [Noisebridge-discuss] I'm working on a
> series of
>     articles about algorithms - data mining
> experts sought asap
> Message-ID:
>     <CAGpv09Vd+1bXJ9A5Yp6qMLujvfkXbBoMae9Ge_MqoVFZPN6Zzw at mail.gmail.com>
> Content-Type: text/plain; charset="windows-1252"
> 
> ---------- Forwarded message ----------
> From: Jim Youll <jim at agentzero.com>
> Date: Sat, Jan 19, 2013 at 3:50 PM
> Subject: [Noisebridge-discuss] I'm working on a series of
> articles about
> algorithms - data mining experts sought asap
> To: noisebridge-discuss at lists.noisebridge.net
> 
> 
> Hi Noisebridge.
> 
> I'm an SF-based tech geek and occasional writer who is
> working on a series
> of articles for Fast Company (http://fastcompany.com/) about the algorithms
> selected in the 2007 paper "Top 10 algorithms in data
> mining" (which is
> locked behind an Springer-Verlag paywall, apropos of other
> more important
> matters going on right now). Citation, abstract, and a list
> of the
> algorithms are pasted at the end of this note; contact me if
> you need a
> copy of the paper, of course.
> 
> Our primary goal is to give Fast Company's readers a quick
> primer on each
> of the algorithms in a way that is accessible to a
> business/non-expert
> audience.
> 
> The articles will be brief, a few hundred words. l'm looking
> for data
> mining / domain experts who  are particularly adept at
> explaining
> complicated math and science in layman's terms. Interviews
> can take place
> in person in SF, via e-mail, IM, Skype, or phone, whatever
> is most
> convenient.
> 
> To elaborate, here is what I'm in search of for each
> algorithm:
>         - "What it does" in plain
> language, maybe with a simple example
>         - How the algorithm changed
> "everyday" practice when it emerged, or
> what it enabled that wasn't possible before
>         - Pointers to companies,
> services, or even /types/ of services
> where the algorithm is likely in operation today
> 
> We can work by e-mail, phone, or Skype - whatever is most
> convenient.
> 
> Please feel free to forward this note to folks whom you
> believe may be a
> good fit for this project.
> 
> An ideal person would have expert knowledge of one or more
> of these
> algorithms, a talent for explaining really technical stuff
> to regular
> people, 20 minutes to spare for an interview, and an
> interest in possibly
> being quoted in a Fast Company article. ;) I know I've seen
> people like
> that around Noisebridge - lots of them - I'm just not sure
> who's available
> in the next week or two.
> 
> Thanks!
> - jim
> jim at agentzero.com
> 
> 
> ----
> 
> Knowl Inf Syst (2008) 14:1?37 DOI 10.1007/s10115-007-0114-2
> SURVEY PAPER
> Top 10 algorithms in data mining
> Xindong Wu ? Vipin Kumar ? J. Ross Quinlan ? Joydeep Ghosh ?
> Qiang Yang ?
> Hiroshi Motoda ? Geoffrey J. McLachlan ? Angus Ng ? Bing Liu
> ? Philip S. Yu
> ? Zhi-Hua Zhou ? Michael Steinbach ? David J. Hand ? Dan
> Steinberg
> Received: 9 July 2007 / Revised: 28 September 2007 /
> Accepted: 8 October
> 2007 Published online: 4 December 2007
> ? Springer-Verlag London Limited 2007
> Abstract This paper presents the top 10 data mining
> algorithms identified
> by the IEEE International Conference on Data Mining (ICDM)
> in December
> 2006: C4.5, k-Means, SVM, Apriori, EM, PageRank, AdaBoost,
> kNN, Naive
> Bayes, and CART. These top 10 algorithms are among the most
> influential
> data mining algorithms in the research community. With each
> algorithm, we
> provide a description of the algorithm, discuss the impact
> of the
> algorithm, and review current and further research on the
> algorithm. These
> 10 algorithms cover classification, clustering, statistical
> learning,
> association analysis, and link mining, which are all among
> the most
> important topics in data mining research and development.
> 
> ALGORITHMS
> 1 C4.5 and beyond
> 2 The k-means algorithm
> 3 Support vector machines
> 4 The Apriori algorithm
> 5 The EM algorithm
> 6 PageRank
> 7 AdaBoost
> 8 kNN: k-nearest neighbor classification
> 9 Naive Bayes
> 10 CART
> 
> 
> 
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> End of ml Digest, Vol 38, Issue 1
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