Difference between revisions of "Autoclass"
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|Short description=Automatic classification or clustering | |Short description=Automatic classification or clustering | ||
|Full description=AutoClass solves the problem of automatic discovery of classes in data (sometimes called clustering or unsupervised learning), as distinct from the generation of class descriptions from labeled examples (called supervised learning). It aims to discover the 'natural' classes in the data. AutoClass is applicable to observations of things that can be described by a set of attributes, without referring to other things. The data values corresponding to each attribute are limited to be either numbers or the elements of a fixed set of symbols. With numeric data, a measurement error must be provided. | |Full description=AutoClass solves the problem of automatic discovery of classes in data (sometimes called clustering or unsupervised learning), as distinct from the generation of class descriptions from labeled examples (called supervised learning). It aims to discover the 'natural' classes in the data. AutoClass is applicable to observations of things that can be described by a set of attributes, without referring to other things. The data values corresponding to each attribute are limited to be either numbers or the elements of a fixed set of symbols. With numeric data, a measurement error must be provided. | ||
| + | |Homepage URL=http://ic-www.arc.nasa.gov/ic/projects/bayes-group/autoclass/ | ||
|User level=none | |User level=none | ||
| − | | | + | |Is High Priority Project=No |
| − | |||
| − | |||
| − | |||
|Computer languages=C | |Computer languages=C | ||
|Documentation note=User intro included; user reference manual included | |Documentation note=User intro included; user reference manual included | ||
| − | | | + | |Decommissioned/Obsolete=No |
| − | | | + | |Accepts cryptocurrency donations=No |
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|Keywords=class,learning,classification,clustering,attribute,value | |Keywords=class,learning,classification,clustering,attribute,value | ||
| − | | | + | |Version identifier=3.3.6 |
| − | |Last review by= | + | |Version date=2009/09/01 |
| − | |Last review date= | + | |Version status=stable |
| − | + | |Version download=https://ti.arc.nasa.gov/m/project/autoclass/autoclass-c-3-3-6.tar.gz | |
| + | |Version comment=3.3.6 stable released 2009-09-01 | ||
| + | |Test entry=No | ||
| + | |Last review by=Bendikker | ||
| + | |Last review date=2018/04/17 | ||
|Submitted date=2011-04-01 | |Submitted date=2011-04-01 | ||
| − | | | + | |Is GNU=No |
| − | + | }} | |
| − | | | + | {{Project license |
| − | | | + | |License=PublicDomain |
| + | |License verified by=Janet Casey | ||
|License verified date=2001-01-31 | |License verified date=2001-01-31 | ||
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}} | }} | ||
{{Person | {{Person | ||
| + | |Real name=James R. Van Zandt | ||
|Role=Maintainer | |Role=Maintainer | ||
| − | |||
|Email=jrv@vanzandt.mv.com | |Email=jrv@vanzandt.mv.com | ||
| − | |Resource URL= | + | }} |
| + | {{Resource | ||
| + | |Resource audience=Python (Ref) | ||
| + | |Resource URL=https://pypi.org/project/autoclass | ||
| + | }} | ||
| + | {{Resource | ||
| + | |Resource audience=Debian (Ref) | ||
| + | |Resource URL=https://tracker.debian.org/pkg/autoclass | ||
}} | }} | ||
{{Software category | {{Software category | ||
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|Mathematics=statistics | |Mathematics=statistics | ||
|Use=mathematics | |Use=mathematics | ||
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}} | }} | ||
{{Software prerequisite | {{Software prerequisite | ||
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|Prerequisite description=glibc | |Prerequisite description=glibc | ||
}} | }} | ||
| + | {{Featured}} | ||
Latest revision as of 12:37, 17 April 2018
Overview
Autoclass
http://ic-www.arc.nasa.gov/ic/projects/bayes-group/autoclass/
Automatic classification or clustering
AutoClass solves the problem of automatic discovery of classes in data (sometimes called clustering or unsupervised learning), as distinct from the generation of class descriptions from labeled examples (called supervised learning). It aims to discover the 'natural' classes in the data. AutoClass is applicable to observations of things that can be described by a set of attributes, without referring to other things. The data values corresponding to each attribute are limited to be either numbers or the elements of a fixed set of symbols. With numeric data, a measurement error must be provided.
Details
Licensing
License
Verified by
Verified on
Notes
Leaders and contributors
| Contact(s) | Role |
|---|---|
| James R. Van Zandt | Maintainer |
Resources and communication
| Audience | Resource type | URI |
|---|---|---|
| Python (Ref) | https://pypi.org/project/autoclass | |
| Debian (Ref) | https://tracker.debian.org/pkg/autoclass |
Software prerequisites
| Kind | Description |
|---|---|
| Required to use | glibc |
About this entry
Permission is granted to copy, distribute and/or modify this document under the terms of the GNU Free Documentation License, Version 1.3 or any later version published by the Free Software Foundation; with no Invariant Sections, no Front-Cover Texts, and no Back-Cover Texts. A copy of the license is included in the page “GNU Free Documentation License”.
The copyright and license notices on this page only apply to the text on this page. Any software or copyright-licenses or other similar notices described in this text has its own copyright notice and license, which can usually be found in the distribution or license text itself.