Concept lattice reduction using different subset of attributes as information granules

Concept lattice reduction using different subset of attributes as information granules In recent years, the output of formal concept analysis has been widely spread in various research fields for knowledge processing tasks. In this process, a major issues arises when large number of formal concepts are generated from the given context. Available approaches lacks in user required dynamic reduction of concept lattice based on shape and size of the given problem. To overcome this problem, the current paper proposes a method to control the size of concept lattice based on user defined subset of attributes (or objects). Further the proposed method provides a way to select some of the important concepts generated from chosen subset of attributes. For this purpose properties of Shannon entropy is utilized by the proposed method to select some of the important concepts at different granulation of their computed weight. The analysis derived from the proposed method is also compared with recently published granulation tree method with an empirical analysis. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Granular Computing Springer Journals

Concept lattice reduction using different subset of attributes as information granules

Loading next page...
 
/lp/springer_journal/concept-lattice-reduction-using-different-subset-of-attributes-as-GeRtbGokv0
Publisher
Springer International Publishing
Copyright
Copyright © 2016 by Springer International Publishing Switzerland
Subject
Engineering; Computational Intelligence; Artificial Intelligence (incl. Robotics)
ISSN
2364-4966
eISSN
2364-4974
D.O.I.
10.1007/s41066-016-0036-z
Publisher site
See Article on Publisher Site

Abstract

In recent years, the output of formal concept analysis has been widely spread in various research fields for knowledge processing tasks. In this process, a major issues arises when large number of formal concepts are generated from the given context. Available approaches lacks in user required dynamic reduction of concept lattice based on shape and size of the given problem. To overcome this problem, the current paper proposes a method to control the size of concept lattice based on user defined subset of attributes (or objects). Further the proposed method provides a way to select some of the important concepts generated from chosen subset of attributes. For this purpose properties of Shannon entropy is utilized by the proposed method to select some of the important concepts at different granulation of their computed weight. The analysis derived from the proposed method is also compared with recently published granulation tree method with an empirical analysis.

Journal

Granular ComputingSpringer Journals

Published: Dec 3, 2016

References

You’re reading a free preview. Subscribe to read the entire article.


DeepDyve is your
personal research library

It’s your single place to instantly
discover and read the research
that matters to you.

Enjoy affordable access to
over 18 million articles from more than
15,000 peer-reviewed journals.

All for just $49/month

Explore the DeepDyve Library

Search

Query the DeepDyve database, plus search all of PubMed and Google Scholar seamlessly

Organize

Save any article or search result from DeepDyve, PubMed, and Google Scholar... all in one place.

Access

Get unlimited, online access to over 18 million full-text articles from more than 15,000 scientific journals.

Your journals are on DeepDyve

Read from thousands of the leading scholarly journals from SpringerNature, Elsevier, Wiley-Blackwell, Oxford University Press and more.

All the latest content is available, no embargo periods.

See the journals in your area

DeepDyve

Freelancer

DeepDyve

Pro

Price

FREE

$49/month
$360/year

Save searches from
Google Scholar,
PubMed

Create lists to
organize your research

Export lists, citations

Read DeepDyve articles

Abstract access only

Unlimited access to over
18 million full-text articles

Print

20 pages / month

PDF Discount

20% off