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  1. International Journal of Machine Learning and Cybernetics
  2. International Journal of Machine Learning and Cybernetics : Volume 8
  3. International Journal of Machine Learning and Cybernetics : Volume 8, Issue 1, February 2017
  4. Concept acquisition approach of object-oriented concept lattices
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International Journal of Machine Learning and Cybernetics : Volume 8
International Journal of Machine Learning and Cybernetics : Volume 8, Issue 6, December 2017
International Journal of Machine Learning and Cybernetics : Volume 8, Issue 5, October 2017
International Journal of Machine Learning and Cybernetics : Volume 8, Issue 4, August 2017
International Journal of Machine Learning and Cybernetics : Volume 8, Issue 3, June 2017
International Journal of Machine Learning and Cybernetics : Volume 8, Issue 2, April 2017
International Journal of Machine Learning and Cybernetics : Volume 8, Issue 1, February 2017
Editorial
Interval sets and three-way concept analysis in incomplete contexts
Three-way conceptual approach for cognitive memory functionalities
Three-way decisions model based on tolerance rough fuzzy set
Multi-level interval-valued fuzzy concept lattices and their attribute reduction
Method for generating decision implication canonical basis based on true premises
Three-way fuzzy concept lattice representation using neutrosophic set
Attribute reduction in inconsistent formal decision contexts based on congruence relations
Decomposition methods of formal contexts to construct concept lattices
Constructing lattice based on irreducible concepts
Concept acquisition approach of object-oriented concept lattices
Attributes reduction and rules acquisition in an lattice-valued information system with fuzzy decision
Multi-scaled concept lattices based on neighborhood systems
Cognitive concept learning from incomplete information
Name identification and extraction with formal concept analysis
Concepts reduction in formal concept analysis with fuzzy setting using Shannon entropy
Closed-set lattice and modular matroid induced by covering-based rough sets
Incremental approaches to knowledge reduction based on characteristic matrices
Improving news articles recommendations via user clustering
Ideals and fuzzy ideals on residuated lattices
N-Dimensional fuzzy hyperideals in semihyperrings
Stability of stochastic fuzzy BAM neural networks with discrete and distributed time-varying delays
A graph optimization method for dimensionality reduction with pairwise constraints
A robust periodicity mining method from incomplete and noisy observations based on relative entropy
A structural information-based twin-hypersphere support vector machine classifier
Optimal sizing of CMOS analog circuits using gravitational search algorithm with particle swarm optimization
Chinese-Naxi machine translation method based on Naxi dependency language model
A comparison of three types of rough fuzzy sets based on two universal sets
Semi-supervised self-training for decision tree classifiers
Speed-up ellipse enclosing character detection approach for large-size document images by parallel scanning and Hough transform
International Journal of Machine Learning and Cybernetics : Volume 7
International Journal of Machine Learning and Cybernetics : Volume 6
International Journal of Machine Learning and Cybernetics : Volume 5
International Journal of Machine Learning and Cybernetics : Volume 4
International Journal of Machine Learning and Cybernetics : Volume 3
International Journal of Machine Learning and Cybernetics : Volume 2
International Journal of Machine Learning and Cybernetics : Volume 1

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Concept acquisition approach of object-oriented concept lattices

Content Provider Springer Nature Link
Author Ma, Jian Min Cai, Ming Jie Zou, Cun Jun
Copyright Year 2016
Abstract Formal concept analysis is an effective tool for data analysis and knowledge discovery. Corresponding to concept lattice in a formal context, object-oriented concept lattice is introduced based on rough set. Obtaining object-oriented concepts is important but difficult because of the higher time complexity. In order to solve this question, we first divide the power set of the attribute set into the layered sets in this paper. Since for any object-oriented concept, the object-oriented concept extension and object-oriented concept intension determine each other uniquely, we introduce the layered extension sets. By discussing the properties of layered extension sets, the approach to acquire object-oriented concepts is investigated, and related concept acquirement algorithm is also depicted. Examples prove that the concept acquirement approach is valid.
Starting Page 123
Ending Page 134
Page Count 12
File Format PDF
ISSN 18688071
Journal International Journal of Machine Learning and Cybernetics
Volume Number 8
Issue Number 1
e-ISSN 1868808X
Language English
Publisher Springer Berlin Heidelberg
Publisher Date 2016-08-06
Publisher Place Berlin, Heidelberg
Access Restriction One Nation One Subscription (ONOS)
Subject Keyword Object-oriented concept lattice Object-oriented attribute concept Layered extension set Concept acquirement approach Concept acquirement algorithm Computational Intelligence Artificial Intelligence (incl. Robotics) Control, Robotics, Mechatronics Complex Systems Systems Biology Pattern Recognition
Content Type Text
Resource Type Article
Subject Artificial Intelligence Computer Vision and Pattern Recognition Software
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