An Incremental Algorithm for Maintaining the Built FUSP Trees Based on the Pre-large Concepts

2014 ◽  
pp. 135-144
Author(s):  
Chun-Wei Lin ◽  
Wensheng Gan ◽  
Tzung-Pei Hong ◽  
Raylin Tso
Keyword(s):  
Incremental Algorithm

scholarly journals Incremental Method of Generating Decision Implication Canonical Basis

2021 ◽  
Author(s):  
Shaoxia Zhang ◽  
Deyu Li ◽  
Yanhui Zhai
Keyword(s):  
Formal Concept Analysis ◽  
Canonical Basis ◽  
Concept Analysis ◽  
Experimental Results ◽  
Incremental Algorithm ◽  
Compact Representation ◽  
Formal Concept ◽  
Practical Applications ◽  
Incremental Method ◽  
Elementary Representation

Abstract Decision implication is an elementary representation of decision knowledge in formal concept analysis. Decision implication canonical basis (DICB), a set of decision implications with completeness and nonredundancy, is the most compact representation of decision implications. The method based on true premises (MBTP) for DICB generation is the most efficient one at present. In practical applications, however, data is always changing dynamically, and MBTP has to re-generate inefficiently the whole DICB. This paper proposes an incremental algorithm for DICB generation, which obtains a new DICB just by modifying and updating the existing one. Experimental results verify that when the samples in data are much more than condition attributes, which is actually a general case in practical applications, the incremental algorithm is significantly superior to MBTP. Furthermore, we conclude that, even for the data in which samples is less than condition attributes, when new samples are continually added into data, the incremental algorithm must be also more efficient than MBTP, because the incremental algorithm just needs to modify the existing DICB, which is only a part of work of MBTP.

A New Incremental Algorithm for Induction of Multivariate Decision Trees for Large Datasets

2008 ◽  
pp. 282-289 ◽  
Author(s):  
Anilu Franco-Arcega ◽  
J. Ariel Carrasco-Ochoa ◽  
Guillermo Sánchez-Díaz ◽  
J. Fco Martínez-Trinidad
Keyword(s):  
Decision Trees ◽  
Large Datasets ◽  
Incremental Algorithm

Towards an Integrative Model of Deductive-Inductive Commonsense Reasoning

2010 ◽  
pp. 245-278
Author(s):  
Xenia Naidenova
Keyword(s):  
Incremental Algorithm ◽  
Integrative Model ◽  
Commonsense Reasoning ◽  
Recursive Procedures ◽  
Good Classification ◽  
Mental Acts ◽  
Made In

The most important steps in the direction to an integrative model of deductive-inductive commonsense reasoning are made in this chapter. The decomposition of inferring good classification tests is advanced into two kinds of subtasks that are in accordance with human mental acts. This decomposition allows modeling incremental inductive-deductive inferences. We give two basic recursive procedures based on two kinds of subtasks for inferring all good maximally redundant classification tests (GMRTs): ASTRA and DIAGaRa. An incremental algorithm INGOMAR for inferring all GMRTs is presented too. The problems of creating an integrative inductive-deductive model of commonsense reasoning are discussed in the last section of this chapter.

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