-->
Seguimi in Twitter Seguimi in Facebook Seguimi in Pinterest Seguimi in LinkedIn Seguimi in Google+ Seguimi  in Stumbleupon seguimi  in instagram Sottoscrivi il feed
Blender, graphic, software, open source, Linux LibreOffice, open source, openoffice Gimp, graphic, software, open source, Linux kernel, Linux, software, open source Linux, distributions, Ubuntu, Linux Mint, Fedora, Mandriva Jamin, gpl, library, open source matroska, multimedia, container, linux pcman, file manager, linux LuninuX, distribition, Linux, open source Linux, infographic, history
Home » , » AutoClass solves the problem of automatic discovery of classes in data.

AutoClass solves the problem of automatic discovery of classes in data.

AutoClass solves the problem of automatic discovery of classes in data.

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.

The AutoClass project 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.

Introduction.

In previous years, the Bayes group at Ames Research Center developed the basic theory and associated algorithms for various kinds of general data analysis techniques. Our earliest efforts were applied to the problem of automatic classification of data. We implemented this theory in the Autoclass series of programs.

autoclass1

AutoClass takes a database of cases described by a combination of real and discrete valued attributes, and automatically finds the natural classes in that data. It does not need to be told how many classes are present or what they look like -- it extracts this information from the data itself.

The classes are described probabilistically, so that an object can have partial membership in the different classes, and the class definitions can overlap. AutoClass generates reports on the classes it has found at the end of its search. AutoClass has been used and tested on many data sets, both within NASA and by industry, academia and other agencies. These applications typically find surprising classifications that show patterns in the data unknown to the user.

Examples include: discovery of new classes of infra-red stars in the IRAS Low Resolution Spectral catalogue (see figure below; and see here and here for more information), new classes of airports in a database of all USA airports, discovery of classes of proteins, introns and other patterns in DNA/protein sequence data, and others.

Key features are:

  • determines the number of classes automatically;
  • can use mixed discrete and real valued data;
  • can handle missing values;
  • processing time is roughly linear in the amount of the data;
  • cases have probabilistic class membership;
  • allows correlation between attributes within a class;
  • generates reports describing the classes found; and
  • predicts "test" case class memberships from a "training" classification.

autoclass

If you liked this article, subscribe to the feed by clicking the image below to keep informed about new contents of the blog:

0 commenti:

Post a Comment

Random Posts

My Blog List

Recent Posts

Recent Posts Widget

Popular Posts

Labels

Archive

Followers

Images Photo Gallery

page counter Mi Ping en TotalPing.com Subscribe using FreeMyFeed
 
Copyright © 2014 Linuxlandit & The Conqueror Penguin