Granular Reduction of Property-Oriented Concept Lattices

Ling, Wei; Zhang, Xiaohua; Qi, Jianjun

doi:10.1007/978-3-642-14197-3_16

Cited by 4 publications

(2 citation statements)

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“…It should be pointed out that the relation among formal, object-oriented, and property-oriented concept lattices was discussed in [ 38 , 39 ], and knowledge reduction of object-oriented and/or property-oriented concept lattices was investigated in [ 7 , 40 , 41 ].…”

Section: Preliminariesmentioning

confidence: 99%

Rule Acquisition in Formal Decision Contexts Based on Formal, Object-Oriented and Property-Oriented Concept Lattices

Ren

Cherukuri

et al. 2014

The Scientific World Journal

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Rule acquisition is one of the main purposes in the analysis of formal decision contexts. Up to now, there have been several types of rules in formal decision contexts such as decision rules, decision implications, and granular rules, which can be viewed as ∧-rules since all of them have the following form: “if conditions 1,2,…, and m hold, then decisions hold.” In order to enrich the existing rule acquisition theory in formal decision contexts, this study puts forward two new types of rules which are called ∨-rules and ∨-∧ mixed rules based on formal, object-oriented, and property-oriented concept lattices. Moreover, a comparison of ∨-rules, ∨-∧ mixed rules, and ∧-rules is made from the perspectives of inclusion and inference relationships. Finally, some real examples and numerical experiments are conducted to compare the proposed rule acquisition algorithms with the existing one in terms of the running efficiency.

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Section: Preliminariesmentioning

confidence: 99%

Rule Acquisition in Formal Decision Contexts Based on Formal, Object-Oriented and Property-Oriented Concept Lattices

Ren

Cherukuri

et al. 2014

The Scientific World Journal

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“…For example, information granules have been studied in rough set theory [17,23,29,30,35,44,46,47] extensively which is considered as one of the approaches of granular computing, and applied in formal concept analysis [10], evidence analysis [33], etc. Recently, studies on combination of granular computing with formal concept analysis have been made by several researchers [7,12,19,31,41,43,45,56]. And what is particularly worth mentioning is that information granules in formal concept analysis mean granular concepts [43] which are the basic concepts used to deduce others.…”

Section: Introductionmentioning

confidence: 99%

Concept learning via granular computing: A cognitive viewpoint

Mei

et al. 2015

Information Sciences

262

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a b s t r a c tConcepts are the most fundamental units of cognition in philosophy and how to learn concepts from various aspects in the real world is the main concern within the domain of conceptual knowledge presentation and processing. In order to improve efficiency and flexibility of concept learning, in this paper we discuss concept learning via granular computing from the point of view of cognitive computing. More precisely, cognitive mechanism of forming concepts is analyzed based on the principles from philosophy and cognitive psychology, including how to model concept-forming cognitive operators, define cognitive concepts and establish cognitive concept structure. Granular computing is then combined with the cognitive concept structure to improve efficiency of concept learning. Furthermore, we put forward a cognitive computing system which is the initial environment to learn composite concepts and can integrate past experiences into itself for enhancing flexibility of concept learning. Also, we investigate cognitive processes whose aims are to deal with the problem of learning one exact or two approximate cognitive concepts from a given object set, attribute set or pair of object and attribute sets.

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