Autoclass

AutoClass is a powerful software solution for automatic discovery of classes in data without the need for labeled examples. It provides an excellent alternative to supervised learning, as it seeks to uncover the natural classes within the data. The project is applicable to observations of things that can be described by a set of attributes without referring to other things. Additionally, the data values corresponding to each attribute are limited to either numbers or the elements of a fixed set of symbols.

One of the highlights of AutoClass is that it does not require the user to provide information on the number of classes present or what they look like. It automatically extracts this information from the data itself, making the process easier and more automated than ever before. The classes are described probabilistically, allowing for an object to have partial membership in different classes, and the class definitions can overlap.

One of the significant contributions of the software is its ability to generate reports on the classes it has found at the end of its search. AutoClass has been tested on many data sets, both within NASA and by industry, academia, and other agencies. Its applications have led to surprising classifications that show patterns in the data unknown to the user.

Ultimately, AutoClass serves as an excellent tool for discovering natural classes within data with ease, allowing users to unlock valuable insights and uncover hidden patterns. Its applications in fields such as astronomy, biology, and economics have made it a dependable and trusted tool amongst professionals, making it a valuable asset to anyone seeking to discover natural classes in their data.