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Posted to user@mahout.apache.org by Ahmed Abdeen Hamed <ah...@gmail.com> on 2012/04/08 21:24:52 UTC
citing mahout
Hello,
Is there a specific format the Mahout developers would like for citing
Mahout?
Thanks very much,
-Ahmed
Re: citing mahout
Posted by Sean Owen <sr...@gmail.com>.
I don't know if there's any particular preferred format. I think you'd
generally cite the web site, and follow any standard citation format for
that.
Sean
On Sun, Apr 8, 2012 at 8:24 PM, Ahmed Abdeen Hamed
<ah...@gmail.com>wrote:
> Hello,
>
> Is there a specific format the Mahout developers would like for citing
> Mahout?
>
> Thanks very much,
>
> -Ahmed
>
Re: citing mahout
Posted by Ted Dunning <te...@gmail.com>.
Beautiful, I was just writing up some clustering work and needed exactly
this.
Thanks!
On Sun, Apr 8, 2012 at 4:54 PM, Manuel Blechschmidt <
Manuel.Blechschmidt@gmx.de> wrote:
> Hi Ahmed,
> I used the following BibTex entry in my Master Thesis:
>
> @webpage{mahout,
> Abstract = {Apache Mahout's goal is to build scalable machine
> learning libraries. With scalable we mean: Scalable to reasonably large
> data sets. Our core algorithms for clustering, classfication and batch
> based collaborative filtering are implemented on top of Apache Hadoop using
> the map/reduce paradigm. However we do not restrict contributions to Hadoop
> based implementations: Contributions that run on a single node or on a
> non-Hadoop cluster are welcome as well. The core libraries are highly
> optimized to allow for good performance also for non-distributed
> algorithms},
> Author = {{Apache Software Foundation}},
> Date-Added = {2011-03-15 13:39:56 +0100},
> Date-Modified = {2011-04-29 14:12:11 +0200},
> Description = { Mahout's goal is to build scalable machine learning
> libraries. With scalable we mean: Scalable to reasonably large data sets.
> Our core algorithms for clustering, classfication and batch based
> collaborative filtering are implemented on top of Apache Hadoop using the
> map/reduce paradigm. However we do not restrict contributions to Hadoop
> based implementations: Contributions that run on a single node or on a
> non-Hadoop cluster are welcome as well. The core libraries are highly
> optimized to allow for good performance also for non-distributed
> algorithms. Scalable to support your business case. Mahout is distributed
> under a commercially friendly Apache Software license. Scalable community.
> The goal of Mahout is to build a vibrant, responsive, diverse community to
> facilitate discussions not only on the project itself but also on potential
> use cases. Come to the mailing lists to find out more.},
> Distribution = {Global},
> Keywords = {apache, apache hadoop, apache hive, apache incubator,
> apache lucene, apache solr, apache taste, apache thrift, apache xml,
> business data mining, cloudbase hadoop, cluster analysis, collaborative
> filtering, data extraction, data filtering, data framework, data
> integration, data matching, data mining, data mining algorithms, data
> mining analysis, data mining data, data mining introduction, data mining
> pdf, data mining software, data mining sql, data mining techniques, data
> representation, data set, data visualization, datamining, distributed solr,
> feature extraction, fuzzy k means, genetic algorithm, hadoop, hadoop
> cluster, hadoop download, hadoop forum, hadoop gfs, hadoop lucene, hadoop
> pig latin, hadoop sequence file, hadoop sequencefile, hierarchical
> clustering, high dimensional, hive hadoop, install solr, introduction to
> data mining, kmeans, knowledge discovery, learning approach, learning
> approaches, learning methods, learning techniques, lucene, machine
> learning, machine translation, mahout apache, mahout taste, map reduce
> hadoop, mining data, mining methods, naive bayes, natural language
> processing, open source search engine, open source search engine software,
> opencms search, org apache lucene, pattern recognition, pattern recognition
> and machine learning, pig apache, pig hadoop, search algorithms, search
> engine, solr api, solr faceted, solr open source, solr search engine, solr
> tika, statistical consulting, statistical data mining, supervised, text
> mining, time series data, unsupervised, web data mining, zookeeper,
> zookeeper apache},
> Lastchecked = {2011-03-15},
> Robots = {index,follow},
> Title = {Apache Mahout:: Scalable machine-learning and data-mining
> library},
> Url = {http://mahout.apache.org},
> Bdsk-Url-1 = {http://mahout.apache.org}}
>
> /Manuel
>
> On 08.04.2012, at 21:24, Ahmed Abdeen Hamed wrote:
>
> > Hello,
> >
> > Is there a specific format the Mahout developers would like for citing
> > Mahout?
> >
> > Thanks very much,
> >
> > -Ahmed
>
> --
> Manuel Blechschmidt
> Dortustr. 57
> 14467 Potsdam
> Mobil: 0173/6322621
> Twitter: http://twitter.com/Manuel_B
>
>
Re: citing mahout
Posted by Ahmed Abdeen Hamed <ah...@gmail.com>.
Thanks you all for your responses!
-Ahmed
On Sun, Apr 8, 2012 at 5:54 PM, Manuel Blechschmidt <
Manuel.Blechschmidt@gmx.de> wrote:
> Hi Ahmed,
> I used the following BibTex entry in my Master Thesis:
>
> @webpage{mahout,
> Abstract = {Apache Mahout's goal is to build scalable machine
> learning libraries. With scalable we mean: Scalable to reasonably large
> data sets. Our core algorithms for clustering, classfication and batch
> based collaborative filtering are implemented on top of Apache Hadoop using
> the map/reduce paradigm. However we do not restrict contributions to Hadoop
> based implementations: Contributions that run on a single node or on a
> non-Hadoop cluster are welcome as well. The core libraries are highly
> optimized to allow for good performance also for non-distributed
> algorithms},
> Author = {{Apache Software Foundation}},
> Date-Added = {2011-03-15 13:39:56 +0100},
> Date-Modified = {2011-04-29 14:12:11 +0200},
> Description = { Mahout's goal is to build scalable machine learning
> libraries. With scalable we mean: Scalable to reasonably large data sets.
> Our core algorithms for clustering, classfication and batch based
> collaborative filtering are implemented on top of Apache Hadoop using the
> map/reduce paradigm. However we do not restrict contributions to Hadoop
> based implementations: Contributions that run on a single node or on a
> non-Hadoop cluster are welcome as well. The core libraries are highly
> optimized to allow for good performance also for non-distributed
> algorithms. Scalable to support your business case. Mahout is distributed
> under a commercially friendly Apache Software license. Scalable community.
> The goal of Mahout is to build a vibrant, responsive, diverse community to
> facilitate discussions not only on the project itself but also on potential
> use cases. Come to the mailing lists to find out more.},
> Distribution = {Global},
> Keywords = {apache, apache hadoop, apache hive, apache incubator,
> apache lucene, apache solr, apache taste, apache thrift, apache xml,
> business data mining, cloudbase hadoop, cluster analysis, collaborative
> filtering, data extraction, data filtering, data framework, data
> integration, data matching, data mining, data mining algorithms, data
> mining analysis, data mining data, data mining introduction, data mining
> pdf, data mining software, data mining sql, data mining techniques, data
> representation, data set, data visualization, datamining, distributed solr,
> feature extraction, fuzzy k means, genetic algorithm, hadoop, hadoop
> cluster, hadoop download, hadoop forum, hadoop gfs, hadoop lucene, hadoop
> pig latin, hadoop sequence file, hadoop sequencefile, hierarchical
> clustering, high dimensional, hive hadoop, install solr, introduction to
> data mining, kmeans, knowledge discovery, learning approach, learning
> approaches, learning methods, learning techniques, lucene, machine
> learning, machine translation, mahout apache, mahout taste, map reduce
> hadoop, mining data, mining methods, naive bayes, natural language
> processing, open source search engine, open source search engine software,
> opencms search, org apache lucene, pattern recognition, pattern recognition
> and machine learning, pig apache, pig hadoop, search algorithms, search
> engine, solr api, solr faceted, solr open source, solr search engine, solr
> tika, statistical consulting, statistical data mining, supervised, text
> mining, time series data, unsupervised, web data mining, zookeeper,
> zookeeper apache},
> Lastchecked = {2011-03-15},
> Robots = {index,follow},
> Title = {Apache Mahout:: Scalable machine-learning and data-mining
> library},
> Url = {http://mahout.apache.org},
> Bdsk-Url-1 = {http://mahout.apache.org}}
>
> /Manuel
>
> On 08.04.2012, at 21:24, Ahmed Abdeen Hamed wrote:
>
> > Hello,
> >
> > Is there a specific format the Mahout developers would like for citing
> > Mahout?
> >
> > Thanks very much,
> >
> > -Ahmed
>
> --
> Manuel Blechschmidt
> Dortustr. 57
> 14467 Potsdam
> Mobil: 0173/6322621
> Twitter: http://twitter.com/Manuel_B
>
>
Re: citing mahout
Posted by Ted Dunning <te...@gmail.com>.
Well, this shorter reference does avoid the problem of having a typo in the
abstract.
On Mon, Apr 9, 2012 at 2:35 AM, Sebastian Schelter <ss...@apache.org> wrote:
> I use a (not so beautiful) very short reference:
>
> @Unpublished{Mahout,
> key = {Apache Mahout},
> title = {Apache {Mahout}, http://mahout.apache.org},
> url = {http://mahout.apache.org}
> }
>
> --sebastian
>
>
> On 08.04.2012 23:54, Manuel Blechschmidt wrote:
> > Hi Ahmed,
> > I used the following BibTex entry in my Master Thesis:
> >
> > @webpage{mahout,
> > Abstract = {Apache Mahout's goal is to build scalable machine
> learning libraries. With scalable we mean: Scalable to reasonably large
> data sets. Our core algorithms for clustering, classfication and batch
> based collaborative filtering are implemented on top of Apache Hadoop using
> the map/reduce paradigm. However we do not restrict contributions to Hadoop
> based implementations: Contributions that run on a single node or on a
> non-Hadoop cluster are welcome as well. The core libraries are highly
> optimized to allow for good performance also for non-distributed
> algorithms},
> > Author = {{Apache Software Foundation}},
> > Date-Added = {2011-03-15 13:39:56 +0100},
> > Date-Modified = {2011-04-29 14:12:11 +0200},
> > Description = { Mahout's goal is to build scalable machine
> learning libraries. With scalable we mean: Scalable to reasonably large
> data sets. Our core algorithms for clustering, classfication and batch
> based collaborative filtering are implemented on top of Apache Hadoop using
> the map/reduce paradigm. However we do not restrict contributions to Hadoop
> based implementations: Contributions that run on a single node or on a
> non-Hadoop cluster are welcome as well. The core libraries are highly
> optimized to allow for good performance also for non-distributed
> algorithms. Scalable to support your business case. Mahout is distributed
> under a commercially friendly Apache Software license. Scalable community.
> The goal of Mahout is to build a vibrant, responsive, diverse community to
> facilitate discussions not only on the project itself but also on potential
> use cases. Come to the mailing lists to find out more.},
> > Distribution = {Global},
> > Keywords = {apache, apache hadoop, apache hive, apache incubator,
> apache lucene, apache solr, apache taste, apache thrift, apache xml,
> business data mining, cloudbase hadoop, cluster analysis, collaborative
> filtering, data extraction, data filtering, data framework, data
> integration, data matching, data mining, data mining algorithms, data
> mining analysis, data mining data, data mining introduction, data mining
> pdf, data mining software, data mining sql, data mining techniques, data
> representation, data set, data visualization, datamining, distributed solr,
> feature extraction, fuzzy k means, genetic algorithm, hadoop, hadoop
> cluster, hadoop download, hadoop forum, hadoop gfs, hadoop lucene, hadoop
> pig latin, hadoop sequence file, hadoop sequencefile, hierarchical
> clustering, high dimensional, hive hadoop, install solr, introduction to
> data mining, kmeans, knowledge discovery, learning approach, learning
> approaches, learning methods, learning techniques, lucene, machine lea
> rning, machine translation, mahout apache, mahout taste, map reduce
> hadoop, mining data, mining methods, naive bayes, natural language
> processing, open source search engine, open source search engine software,
> opencms search, org apache lucene, pattern recognition, pattern recognition
> and machine learning, pig apache, pig hadoop, search algorithms, search
> engine, solr api, solr faceted, solr open source, solr search engine, solr
> tika, statistical consulting, statistical data mining, supervised, text
> mining, time series data, unsupervised, web data mining, zookeeper,
> zookeeper apache},
> > Lastchecked = {2011-03-15},
> > Robots = {index,follow},
> > Title = {Apache Mahout:: Scalable machine-learning and data-mining
> library},
> > Url = {http://mahout.apache.org},
> > Bdsk-Url-1 = {http://mahout.apache.org}}
> >
> > /Manuel
> >
> > On 08.04.2012, at 21:24, Ahmed Abdeen Hamed wrote:
> >
> >> Hello,
> >>
> >> Is there a specific format the Mahout developers would like for citing
> >> Mahout?
> >>
> >> Thanks very much,
> >>
> >> -Ahmed
> >
>
>
Re: citing mahout
Posted by Sebastian Schelter <ss...@apache.org>.
I use a (not so beautiful) very short reference:
@Unpublished{Mahout,
key = {Apache Mahout},
title = {Apache {Mahout}, http://mahout.apache.org},
url = {http://mahout.apache.org}
}
--sebastian
On 08.04.2012 23:54, Manuel Blechschmidt wrote:
> Hi Ahmed,
> I used the following BibTex entry in my Master Thesis:
>
> @webpage{mahout,
> Abstract = {Apache Mahout's goal is to build scalable machine learning libraries. With scalable we mean: Scalable to reasonably large data sets. Our core algorithms for clustering, classfication and batch based collaborative filtering are implemented on top of Apache Hadoop using the map/reduce paradigm. However we do not restrict contributions to Hadoop based implementations: Contributions that run on a single node or on a non-Hadoop cluster are welcome as well. The core libraries are highly optimized to allow for good performance also for non-distributed algorithms},
> Author = {{Apache Software Foundation}},
> Date-Added = {2011-03-15 13:39:56 +0100},
> Date-Modified = {2011-04-29 14:12:11 +0200},
> Description = { Mahout's goal is to build scalable machine learning libraries. With scalable we mean: Scalable to reasonably large data sets. Our core algorithms for clustering, classfication and batch based collaborative filtering are implemented on top of Apache Hadoop using the map/reduce paradigm. However we do not restrict contributions to Hadoop based implementations: Contributions that run on a single node or on a non-Hadoop cluster are welcome as well. The core libraries are highly optimized to allow for good performance also for non-distributed algorithms. Scalable to support your business case. Mahout is distributed under a commercially friendly Apache Software license. Scalable community. The goal of Mahout is to build a vibrant, responsive, diverse community to facilitate discussions not only on the project itself but also on potential use cases. Come to the mailing lists to find out more.},
> Distribution = {Global},
> Keywords = {apache, apache hadoop, apache hive, apache incubator, apache lucene, apache solr, apache taste, apache thrift, apache xml, business data mining, cloudbase hadoop, cluster analysis, collaborative filtering, data extraction, data filtering, data framework, data integration, data matching, data mining, data mining algorithms, data mining analysis, data mining data, data mining introduction, data mining pdf, data mining software, data mining sql, data mining techniques, data representation, data set, data visualization, datamining, distributed solr, feature extraction, fuzzy k means, genetic algorithm, hadoop, hadoop cluster, hadoop download, hadoop forum, hadoop gfs, hadoop lucene, hadoop pig latin, hadoop sequence file, hadoop sequencefile, hierarchical clustering, high dimensional, hive hadoop, install solr, introduction to data mining, kmeans, knowledge discovery, learning approach, learning approaches, learning methods, learning techniques, lucene, machine lea
rning, machine translation, mahout apache, mahout taste, map reduce hadoop, mining data, mining methods, naive bayes, natural language processing, open source search engine, open source search engine software, opencms search, org apache lucene, pattern recognition, pattern recognition and machine learning, pig apache, pig hadoop, search algorithms, search engine, solr api, solr faceted, solr open source, solr search engine, solr tika, statistical consulting, statistical data mining, supervised, text mining, time series data, unsupervised, web data mining, zookeeper, zookeeper apache},
> Lastchecked = {2011-03-15},
> Robots = {index,follow},
> Title = {Apache Mahout:: Scalable machine-learning and data-mining library},
> Url = {http://mahout.apache.org},
> Bdsk-Url-1 = {http://mahout.apache.org}}
>
> /Manuel
>
> On 08.04.2012, at 21:24, Ahmed Abdeen Hamed wrote:
>
>> Hello,
>>
>> Is there a specific format the Mahout developers would like for citing
>> Mahout?
>>
>> Thanks very much,
>>
>> -Ahmed
>
Re: citing mahout
Posted by Manuel Blechschmidt <Ma...@gmx.de>.
Hi Ahmed,
I used the following BibTex entry in my Master Thesis:
@webpage{mahout,
Abstract = {Apache Mahout's goal is to build scalable machine learning libraries. With scalable we mean: Scalable to reasonably large data sets. Our core algorithms for clustering, classfication and batch based collaborative filtering are implemented on top of Apache Hadoop using the map/reduce paradigm. However we do not restrict contributions to Hadoop based implementations: Contributions that run on a single node or on a non-Hadoop cluster are welcome as well. The core libraries are highly optimized to allow for good performance also for non-distributed algorithms},
Author = {{Apache Software Foundation}},
Date-Added = {2011-03-15 13:39:56 +0100},
Date-Modified = {2011-04-29 14:12:11 +0200},
Description = { Mahout's goal is to build scalable machine learning libraries. With scalable we mean: Scalable to reasonably large data sets. Our core algorithms for clustering, classfication and batch based collaborative filtering are implemented on top of Apache Hadoop using the map/reduce paradigm. However we do not restrict contributions to Hadoop based implementations: Contributions that run on a single node or on a non-Hadoop cluster are welcome as well. The core libraries are highly optimized to allow for good performance also for non-distributed algorithms. Scalable to support your business case. Mahout is distributed under a commercially friendly Apache Software license. Scalable community. The goal of Mahout is to build a vibrant, responsive, diverse community to facilitate discussions not only on the project itself but also on potential use cases. Come to the mailing lists to find out more.},
Distribution = {Global},
Keywords = {apache, apache hadoop, apache hive, apache incubator, apache lucene, apache solr, apache taste, apache thrift, apache xml, business data mining, cloudbase hadoop, cluster analysis, collaborative filtering, data extraction, data filtering, data framework, data integration, data matching, data mining, data mining algorithms, data mining analysis, data mining data, data mining introduction, data mining pdf, data mining software, data mining sql, data mining techniques, data representation, data set, data visualization, datamining, distributed solr, feature extraction, fuzzy k means, genetic algorithm, hadoop, hadoop cluster, hadoop download, hadoop forum, hadoop gfs, hadoop lucene, hadoop pig latin, hadoop sequence file, hadoop sequencefile, hierarchical clustering, high dimensional, hive hadoop, install solr, introduction to data mining, kmeans, knowledge discovery, learning approach, learning approaches, learning methods, learning techniques, lucene, machine learning, machine translation, mahout apache, mahout taste, map reduce hadoop, mining data, mining methods, naive bayes, natural language processing, open source search engine, open source search engine software, opencms search, org apache lucene, pattern recognition, pattern recognition and machine learning, pig apache, pig hadoop, search algorithms, search engine, solr api, solr faceted, solr open source, solr search engine, solr tika, statistical consulting, statistical data mining, supervised, text mining, time series data, unsupervised, web data mining, zookeeper, zookeeper apache},
Lastchecked = {2011-03-15},
Robots = {index,follow},
Title = {Apache Mahout:: Scalable machine-learning and data-mining library},
Url = {http://mahout.apache.org},
Bdsk-Url-1 = {http://mahout.apache.org}}
/Manuel
On 08.04.2012, at 21:24, Ahmed Abdeen Hamed wrote:
> Hello,
>
> Is there a specific format the Mahout developers would like for citing
> Mahout?
>
> Thanks very much,
>
> -Ahmed
--
Manuel Blechschmidt
Dortustr. 57
14467 Potsdam
Mobil: 0173/6322621
Twitter: http://twitter.com/Manuel_B