Application of fuzzy rule induction to data mining

1998 ◽  
pp. 260-271 ◽  
Author(s):  
Christophe Marsala
Keyword(s):  
Data Mining ◽  
Fuzzy Rule ◽  
Rule Induction

A novel algorithm for fuzzy rule induction in data mining

2013 ◽  
Vol 228 (5) ◽  
pp. 877-895 ◽  
Author(s):  
AA Afify
Keyword(s):  
Data Mining ◽  
Predictive Accuracy ◽  
Fuzzy Rule ◽  
Rule Induction ◽  
Fuzzy Classification ◽  
Automatic Identification ◽  
Classification Rules ◽  
Fuzzy Techniques ◽  
Fuzzy Learning ◽  
Novel Algorithm

Rule induction as a method of constructing classifiers is of particular interest to data mining because it generates models in the form of If-Then rules which are more expressive and easier for humans to comprehend and check. Several induction algorithms have been developed to learn classification rules. However, most of these algorithms are based on ‘crisp’ data and produce ‘crisp’ models. This paper presents FuzzySRI, a novel algorithm based on the techniques of fuzzy sets and fuzzy logic for inducing fuzzy classification rules. The algorithm possesses the clear knowledge representation capability of rule induction methods and the ability of fuzzy techniques to handle vague information. Experimental results show that FuzzySRI can outperform other fuzzy and non-fuzzy learning systems in terms of predictive accuracy, comprehensibility, and computational efficiency. It is also shown that FuzzySRI can be successfully applied to an industrial application concerning the automatic identification of machine faults.

scholarly journals FURIA: an algorithm for unordered fuzzy rule induction

2009 ◽  
Vol 19 (3) ◽  
pp. 293-319 ◽  
Author(s):  
Jens Hühn ◽  
Eyke Hüllermeier
Keyword(s):  
Fuzzy Rule ◽  
Rule Induction

Introduction to Fuzzy Data Mining Methods

2011 ◽  
pp. 55-95 ◽  
Author(s):  
Balazs Feil ◽  
Janos Abonyi
Keyword(s):  
Data Mining ◽  
Model Performance ◽  
Fuzzy Rule ◽  
Classification Problem ◽  
Data Sets ◽  
Rule Based ◽  
Data Mining Algorithms ◽  
Rule Bases ◽  
Mining Algorithms ◽  
Extraction Model

This chapter aims to give a comprehensive view about the links between fuzzy logic and data mining. It will be shown that knowledge extracted from simple data sets or huge databases can be represented by fuzzy rule-based expert systems. It is highlighted that both model performance and interpretability of the mined fuzzy models are of major importance, and effort is required to keep the resulting rule bases small and comprehensible. Therefore, in the previous years, soft computing based data mining algorithms have been developed for feature selection, feature extraction, model optimization, and model reduction (rule based simplification). Application of these techniques is illustrated using the wine data classification problem. The results illustrate that fuzzy tools can be applied in a synergistic manner through the nine steps of knowledge discovery.

Designing Component-Based Heuristic Search Engines for Knowledge Discovery

2011 ◽  
pp. 68-88
Author(s):  
Craig M. Howard
Keyword(s):  
Operating System ◽  
Data Mining ◽  
Knowledge Discovery ◽  
Search Engines ◽  
Rule Induction ◽  
Object Oriented Design ◽  
Modular Programming ◽  
Software Packages ◽  
Component Object ◽  
Microsoft Windows

The overall size of software packages has grown considerably over recent years. Modular programming, object-oriented design and the use of static and dynamic libraries have all contributed towards the reusability and maintainability of these packages. One of the latest methodologies that aims to further improve software design is the use of component-based services. The Component Object Model (COM) is a specification that provides a standard for writing software components that are easily interoperable. The most common platform for component libraries is on Microsoft Windows, where COM objects are an integral part of the operating system and used extensively in most major applications. This chapter examines the use of COM in the design of search engines for knowledge discovery and data mining using modern heuristic techniques and how adopting this approach benefits the design of a commercial toolkit. The chapter describes how search engines have been implemented as COM objects and how representation and problem components have been created to solve rule induction problems in data mining.

A fuzzy rule induction method using genetic algorithm

1996 ◽  
Vol 18 (2-3) ◽  
pp. 135-145 ◽  
Author(s):  
Toshio Tsuchiya ◽  
Tatsushi Maeda ◽  
Yukihiro Matsubara ◽  
Mitsuo Nagamachi
Keyword(s):  
Genetic Algorithm ◽  
Fuzzy Rule ◽  
Rule Induction ◽  
Induction Method

Optimization of Mean and Standard Deviation of Multiple Responses Using Patient Rule Induction Method

2018 ◽  
Vol 14 (1) ◽  
pp. 60-74 ◽  
Author(s):  
Jin-Kyung Yang ◽  
Dong-Hee Lee
Keyword(s):  
Data Mining ◽  
Standard Deviation ◽  
Large Volume ◽  
Rule Induction ◽  
Induction Method ◽  
Multiple Responses ◽  
Case Examples ◽  
Product And Process ◽  
Patient Rule Induction Method ◽  
Operational Data

In product and process optimization, it is common to have multiple responses to be optimized. This is called multi-response optimization (MRO). When optimizing multiple responses, it is important to consider variability as well as mean of the multiple responses. The authors call this problem as extended MRO (EMRO) where both of mean and variability of the multiple responses are optimized. In this article, they propose a data mining approach to EMRO. In these days, analyzing a large volume of operational data is getting attention due to the development of data processing techniques. Traditional MRO methods takes a model-based approach. However, this approach has limitations when dealing with a large volume of operational data. The authors propose a particular data mining method by modifying patient rule induction method for EMRO. The proposed method obtains an optimal setting of the input variables directly from the operational data where mean and standard deviation of multiple responses are optimized. The authors explain a detailed procedure of the proposed method with case examples.

Sign in / Sign up

Export Citation Format

Share Document