Toward Rough Sets Based Rule Generation from Tables with Uncertain Numerical Values

2010 ◽  
pp. 395-406
Author(s):  
Hiroshi Sakai ◽  
Michinori Nakata ◽  
Dominik Ślȩzak
Keyword(s):  
Rough Sets ◽  
Rule Generation

Rough Sets and Association Rule Generation

1999 ◽  
Vol 40 (4) ◽  
pp. 383-405 ◽  
Author(s):  
Hung Son Nguyen ◽  
Sinh Hoa Nguyen
Keyword(s):  
Association Rule ◽  
Rough Sets ◽  
Rule Generation

Rough Sets Based Rule Generation from Data with Categorical and Numerical Values

2008 ◽  
Vol 12 (5) ◽  
pp. 426-434 ◽  
Author(s):  
Hiroshi Sakai ◽  
◽  
Kazuhiro Koba ◽  
Michinori Nakata ◽  
Keyword(s):  
Machine Learning ◽  
Set Theory ◽  
Rough Set ◽  
Rough Sets ◽  
Rough Set Theory ◽  
Rule Generation ◽  
Utility Program ◽  
New Framework

Rough set theory has been mainly applied to data with categorical values. In order to handle data with numerical values in this theory, a familiar concept of ‘wildcards’ was employed, and a new framework of rough sets based rule generation has been proposed. Two characters @ and # were introduced into this framework, and numerical patterns were also defined for numerical values. The concepts of ‘coarse’ and ‘fine’ for rules were explicitly defined according to numerical patterns. This paper enhances the previous framework, and describes the implementation of an utility program. This utility program is applied to the data in UCI Machine Learning Repository, and some useful rules are obtained.

scholarly journals Modeling Decisions for Artificial Intelligence

2008 ◽  
Vol 12 (5) ◽  
pp. 408-408
Author(s):  
Vicenç Torra ◽  
◽  
Yasuo Narukawa ◽  
Keyword(s):  
Artificial Intelligence ◽  
Fuzzy Clustering ◽  
Rough Sets ◽  
Decision Procedure ◽  
Clustering Algorithms ◽  
Decision Makers ◽  
Rule Generation ◽  
Special Issue ◽  
Weather Information ◽  
The Third

In August 2007, the 4th International Conference on Modeling Decisions for Artificial Intelligence (MDAI1) was held in Kitakyushu, Japan. This special issue has its origins in the conference. We present nine papers related to soft computing tool applications. The first paper, by Honda and Okazaki, presents an axiomatization of a generalized Shaply value. The second paper, by García-Lapresta, also related to decision-making, introduces a multistage decision procedure in which decision-makers opinions are weighted by their contribution to an agreement. The third paper, by Torra and Miyamoto, concerns the problem of loading a container, outlining a system for loading nonorthogonal objects. The fourth paper, by Sakai, Koba, and Nakata, is devoted to rule generation based on rough sets. The fifth paper by Hiramatsu, Huynh, and Nakamori, deals with a fuzzy-based model applied to weather information. The sixth paper, by Inokuchi and Miyamoto, discuss fuzzy clustering algorithms for discrete data. The seventh paper, by Miyamoto, Kuroda, and Arai, studies an algorithm for the sequential extraction of clusters compared to mountain clustering. In the eighth paper, Miyamoto formulates fuzzy clustering using the calculus of variations. The ningth and final paper treats fuzzy clustering, in which Endo et al. discuss fuzzy c-means for data with tolerance. In closing, we thank the referees for their work on reviews and Prof. Hirota for editing this special issue. We also thank the Fuji Technology Press Ltd. staff for its advice. 1Work partially funded by Spanish MEC (projects ARES – CONSOLIDER INGENIO 2010 CSD2007-00004 – and eAEGIS – TSI2007-65406-C03-02)

On Two Apriori-Based Rule Generators: Apriori in Prolog and Apriori in SQL

2018 ◽  
Vol 22 (3) ◽  
pp. 394-403 ◽  
Author(s):  
Hiroshi Sakai ◽  
Kao-Yi Shen ◽  
Michinori Nakata ◽  
◽  
◽  
...  
Keyword(s):  
Rough Sets ◽  
Equivalence Classes ◽  
The Other ◽  
Data Sets ◽  
Apriori Algorithm ◽  
Rule Generation ◽  
Other Hand ◽  
Table Data ◽  
The One ◽  
Existing Data

This paper focuses on two Apriori-based rule generators. The first is the rule generator in Prolog and C, and the second is the one in SQL. They are namedApriori in PrologandApriori in SQL, respectively. Each rule generator is based on the Apriori algorithm. However, each rule generator has its own properties. Apriori in Prolog employs the equivalence classes defined by table data sets and follows the framework of rough sets. On the other hand, Apriori in SQL employs a search for rule generation and does not make use of equivalence classes. This paper clarifies the properties of these two rule generators and considers effective applications of each to existing data sets.

An Application of Discernibility Functions to Generating Minimal Rules in Non-Deterministic Information Systems

2006 ◽  
Vol 10 (5) ◽  
pp. 695-702 ◽  
Author(s):  
Hiroshi Sakai ◽  
◽  
Michinori Nakata ◽  
Keyword(s):  
Information Systems ◽  
Rough Sets ◽  
Rule Generation

Minimal rule generation in Non-deterministic Information Systems (NISs), which follows rough sets based rule generation in Deterministic Information Systems (DISs), is presented. According to certain rules and possible rules in NISs, minimal certain rules and minimal possible rules are defined. Discernibility functions are also introduced into NISs for generating minimal certain rules. Like minimal rule generation in DISs, the condition part of a minimal certain rule is given as a solution of an introduced discernibility function. As for generating minimal possible rules, there may be lots of discernibility functions to be solved. So, an algorithm based on an order of attributes is proposed. A tool, which generates minimal certain rules and minimal possible rules, has also been implemented.

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