Storing and analysing fuzzy data from surveys by relational databases and fuzzy logic approaches

2015 ◽  
Author(s):  
Miroslav Hudec
Keyword(s):  
Fuzzy Logic ◽  
Relational Databases ◽  
Fuzzy Data

XML Modeling of Fuzzy Data with Relational Databases

2011 ◽  
Vol 34 (2) ◽  
pp. 291-303 ◽  
Author(s):  
Li YAN ◽  
Zong-Min MA ◽  
Jian LIU ◽  
Fu ZHANG
Keyword(s):  
Relational Databases ◽  
Fuzzy Data

Comparison of Linguistic Summaries and Fuzzy Functional Dependencies Related to Data Mining

2014 ◽  
pp. 174-203 ◽  
Author(s):  
Miroslav Hudec ◽  
Miljan Vučetić ◽  
Mirko Vujošević
Keyword(s):  
Data Mining ◽  
Fuzzy Logic ◽  
Relational Databases ◽  
Missing Values ◽  
Expert Knowledge ◽  
Real Data ◽  
Research Area ◽  
Functional Dependencies ◽  
Useful Knowledge ◽  
Important Research Area

Data mining methods based on fuzzy logic have been developed recently and have become an increasingly important research area. In this chapter, the authors examine possibilities for discovering potentially useful knowledge from relational database by integrating fuzzy functional dependencies and linguistic summaries. Both methods use fuzzy logic tools for data analysis, acquiring, and representation of expert knowledge. Fuzzy functional dependencies could detect whether dependency between two examined attributes in the whole database exists. If dependency exists only between parts of examined attributes' domains, fuzzy functional dependencies cannot detect its characters. Linguistic summaries are a convenient method for revealing this kind of dependency. Using fuzzy functional dependencies and linguistic summaries in a complementary way could mine valuable information from relational databases. Mining intensities of dependencies between database attributes could support decision making, reduce the number of attributes in databases, and estimate missing values. The proposed approach is evaluated with case studies using real data from the official statistics. Strengths and weaknesses of the described methods are discussed. At the end of the chapter, topics for further research activities are outlined.

Comparisons on Reliability of Likert Scale between Crisp and Fuzzy Data

2014 ◽  
Vol 635-637 ◽  
pp. 874-877
Author(s):  
Yuan Horng Lin ◽  
Jeng Ming Yih
Keyword(s):  
Fuzzy Logic ◽  
Survey Data ◽  
Likert Scale ◽  
Future Research ◽  
Fuzzy Data ◽  
Questionnaire Data ◽  
Human Thinking ◽  
Better Than

The purpose of this study is to compare the reliability of Likert scale between crisp and fuzzy data. The survey data is simulated based on two kinds of questionnaire data. They are questionnaire of crisp data and fuzzy data respectively. According to the viewpoints of fuzzy logic, human thinking is multi-value and fuzzy data will be more appropriate for survey. Therefore, it is proposed that the reliability from fuzzy data will be higher. Results of the simulation show that reliability of fuzzy data performs better than crisp data. Based on the findings of this study, some suggestions and recommendations are discussed for future research.

scholarly journals Data Mining and Fuzzy Data Mining Using MapReduce Algorithms

2021 ◽  
Author(s):  
Poli Venkata Subba Reddy
Keyword(s):  
Data Mining ◽  
Fuzzy Logic ◽  
Original Data ◽  
Space Complexity ◽  
Fuzzy Data ◽  
Discovery Process ◽  
Web Data ◽  
Web Data Mining ◽  
Inexact Information ◽  
Time And Space Complexity

Data mining is knowledge discovery process. It has to deal with exact information and inexact information. Statistical methods deal with inexact information but it is based on likelihood. Zadeh fuzzy logic deals with inexact information but it is based on belief and it is simple to use. Fuzzy logic is used to deal with inexact information. Data mining consist methods and classifications. These methods and classifications are discussed for both exact and inexact information. Retrieval of information is important in data mining. The time and space complexity is high in big data. These are to be reduced. The time complexity is reduced through the consecutive retrieval (C-R) property and space complexity is reduced with blackboard systems. Data mining for web data based is discussed. In web data mining, the original data have to be disclosed. Fuzzy web data mining is discussed for security of data. Fuzzy web programming is discussed. Data mining, fuzzy data mining, and web data mining are discussed through MapReduce algorithms.

Flexible queries on relational databases using fuzzy logic and ontologies

2016 ◽  
Vol 366 ◽  
pp. 150-164 ◽  
Author(s):  
Carmen Martínez-Cruz ◽  
José M. Noguera ◽  
M. Amparo Vila
Keyword(s):  
Fuzzy Logic ◽  
Relational Databases ◽  
Flexible Queries

How to Achieve Fuzzy Relational Databases Managing Fuzzy Data and Metadata

2011 ◽  
pp. 351-380 ◽  
Author(s):  
Mohamed Ali Ben Hassine ◽  
Amel Grissa Touzi ◽  
José Galindo ◽  
Habib Ounelli
Keyword(s):  
Data Mining ◽  
Relational Databases ◽  
Fuzzy Data ◽  
Imprecise Data ◽  
The Third ◽  
Fuzzy Database ◽  
Existing Data ◽  
Flexible Querying ◽  
Mining Tools ◽  
Total Migration

Fuzzy relational databases have been introduced to deal with uncertain or incomplete information demonstrating the efficiency of processing fuzzy queries. For these reasons, many organizations aim to integrate flexible querying to handle imprecise data or to use fuzzy data mining tools, minimizing the transformation costs. The best solution is to offer a smooth migration towards this technology. This chapter presents a migration approach from relational databases towards fuzzy relational databases. This migration is divided into three strategies. The first one, named “partial migration,” is useful basically to include fuzzy queries in classic databases without changing existing data. It needs some definitions (fuzzy metaknowledge) in order to treat fuzzy queries written in FSQL language (Fuzzy SQL). The second one, named “total migration,” offers in addition to the flexible querying, a real fuzzy database, with the possibility to store imprecise data. This strategy requires a modification of schemas, data, and eventually programs. The third strategy is a mixture of the previous strategies, generally as a temporary step, easier and faster than the total migration.

FSQL and SQLf

2011 ◽  
pp. 270-298 ◽  
Author(s):  
Angélica Urrutia ◽  
Leonid Tineo ◽  
Claudia Gonzalez
Keyword(s):  
Fuzzy Logic ◽  
System Architecture ◽  
Relational Databases ◽  
Experimental Performance ◽  
Satisfaction Degree

Actually, FSQL and SQLf are the main fuzzy logic based proposed extensions to SQL. It would be very interesting to integrate them with a standard for fuzzy databases. The issue is what to take from one or other proposal. In this chapter, we analyze FSQL and SQLf making a comparison in several ways: approach direction, fuzzy components, system architecture, satisfaction degree, evaluation mechanisms, and experimental performance. We observe that there are powerful and interesting features in both proposals that could be mixed in a unified language for fuzzy relational databases.

ON THE ROLE OF INTERPRETABILITY IN FUZZY DATA MINING

2007 ◽  
Vol 15 (05) ◽  
pp. 521-537 ◽  
Author(s):  
CORRADO MENCAR ◽  
GIOVANNA CASTELLANO ◽  
ANNA M. FANELLI
Keyword(s):  
Data Mining ◽  
Fuzzy Logic ◽  
Knowledge Discovery ◽  
End Users ◽  
Careful Consideration ◽  
Fuzzy Data ◽  
Systematic Methodology ◽  
Mining Methods

Data Mining, a central step in the broader overall process of Knowledge Discovery from Databases, concerns with discovering useful properties, called patterns, from data. Understandability is an essential — yet rarely tackled — feature that makes resulting patterns accessible by end users. In this paper we argue that the adoption of Fuzzy Logic for Data Mining can improve understandability of derived patterns. Indeed, Fuzzy Logic is able to represent concepts in a “human-centric” way. Hence, Data Mining methods based on Fuzzy Logic may potentially meet the so-called “Comprehensibility Postulate”, which characterizes the blurry notion of understandability. However, the mere adoption of Fuzzy Logic for Data Mining is not enough to achieve understandability. This paper describes and comments a number of issues that need to be addressed to provide for understandable patterns. A careful consideration of all such issues may end up in a systematic methodology to discover comprehensible knowledge from data.

Formalising PFSQL queries using ŁΠ fuzzy logic

2011 ◽  
Vol 22 (3) ◽  
pp. 533-547 ◽  
Author(s):  
ALEKSANDAR PEROVIĆ ◽  
ALEKSANDAR TAKAČI ◽  
SRDJAN ŠKRBIĆ
Keyword(s):  
Fuzzy Logic ◽  
Constraint Satisfaction ◽  
Relational Databases ◽  
Query Language ◽  
Constraint Satisfaction Problem ◽  
Current Paper ◽  
Conservative Extension ◽  
Logic Formula ◽  
Structured Query Language

Using the concept of a generalised priority constraint satisfaction problem, we previously found a way to introduce priority queries into fuzzy relational databases. The results were PFSQL (Priority Fuzzy Structured Query Language) together with a database independent interpreter for it. In an effort to improve the performance of the resolution of PFSQL queries, the aim of the current paper is to formalise PFSQL queries by obtaining their interpretation in an existing fuzzy logic. We have found that the ŁΠ logic provides sufficient elements. The SELECT line of PFSQL queries is semantically a formula of some fuzzy logic, and we show that such formulas can be naturally expressed in a conservative extension of the ŁΠ logic. Furthermore, we prove a theorem that gives the PSPACE containment for the complexity of finding a model for a given ŁΠ logic formula.

Sign in / Sign up

Export Citation Format

Share Document