On the Usefulness of Fuzzy Sets in Data Mining

2009 ◽  
pp. 457-470
Author(s):  
Eyke Hüllermeier
Keyword(s):  
Data Mining ◽  
Fuzzy Sets

Data Discovery Approaches for Vague Spatial Data

Data Mining ◽  
2013 ◽  
pp. 50-65
Author(s):  
Frederick E. Petry
Keyword(s):  
Data Mining ◽  
Fuzzy Sets ◽  
Association Rules ◽  
Rough Set ◽  
Fuzzy Set ◽  
Spatial Data ◽  
Spatial Databases ◽  
Uncertain Data ◽  
Rule Extraction ◽  
Data Discovery

This chapter focuses on the application of the discovery of association rules in approaches vague spatial databases. The background of data mining and uncertainty representations using rough set and fuzzy set techniques is provided. The extensions of association rule extraction for uncertain data as represented by rough and fuzzy sets is described. Finally, an example of rule extraction for both types of uncertainty representations is given.

Fuzzy Classification in Shipwreck Scatter Analysis

2011 ◽  
pp. 516-537 ◽  
Author(s):  
Yauheni Veryha ◽  
Jean-Yves Blot ◽  
Joao Coelho
Keyword(s):  
Data Mining ◽  
Information Systems ◽  
Fuzzy Sets ◽  
Relational Databases ◽  
Maritime Archaeology ◽  
Fuzzy Classification ◽  
Fuzzy Sets Theory ◽  
Report Generation ◽  
Classification Framework ◽  
Sets Theory

There are many well-known applications of fuzzy sets theory in various fields of science and technology. However, we think that the area of maritime archaeology did not attract enough attention from researchers of fuzzy sets theory in the last decades. In this chapter, we present examples of problems arising in shipwreck scatter analysis where fuzzy classification may be very useful. Using a real-world example of fragments of ceramics from an ancient shipwreck, we present an exemplary application of the fuzzy classification framework with SQL querying for data mining in archaeological information systems. Our framework can be used as a data mining tool. It can be relatively easily integrated with conventional relational databases, which are widely used in existing archaeological information systems. The main benefits of using our fuzzy classification approach include flexible and precise data analysis with userfriendly information presentation at the report generation phase.

Representation by levels: An alternative to fuzzy sets for fuzzy data mining

2020 ◽  
Vol 401 ◽  
pp. 113-132
Author(s):  
Carlos Molina ◽  
M. Dolores Ruiz ◽  
José M. Serrano
Keyword(s):  
Data Mining ◽  
Fuzzy Sets ◽  
Fuzzy Data

Critical Parameters for Fuzzy Data Mining

2015 ◽  
pp. 1-18
Author(s):  
Sinchan Bhattacharya ◽  
Vishal Bhatnagar
Keyword(s):  
Data Mining ◽  
Fuzzy Sets ◽  
High Performance ◽  
Rough Set Theory ◽  
Inductive Logic ◽  
Critical Parameters ◽  
Maximum Efficiency ◽  
Fuzzy Data ◽  
Use Of Data ◽  
Performance Computing

Research on data mining is increasing at an incessant rate and to improve its effectiveness other techniques have been applied such as fuzzy sets, rough set theory, knowledge representation, inductive logic programming, or high-performance computing. Fuzzy logic due to its proficiency in handling uncertainty has gained its importance in a variety of applications in combination with the use of data mining techniques. In this chapter we take this association a notch further by examining the parameters which allow fuzzy sets and data mining to be combined into what has come to be known as fuzzy data mining. Analyzing and understanding these critical parameters is the main purpose of this chapter, so as to acquire maximum efficiency in applying the same which impelled the authors to work extensively and find out the crucial parameters essential to the application of fuzzy data mining.

Critical Parameters for Fuzzy Data Mining

2013 ◽  
pp. 230-247
Author(s):  
Sinchan Bhattacharya ◽  
Vishal Bhatnagar
Keyword(s):  
Data Mining ◽  
Fuzzy Sets ◽  
High Performance ◽  
Rough Set Theory ◽  
Inductive Logic ◽  
Critical Parameters ◽  
Maximum Efficiency ◽  
Fuzzy Data ◽  
Use Of Data ◽  
Performance Computing

Research on data mining is increasing at an incessant rate and to improve its effectiveness other techniques have been applied such as fuzzy sets, rough set theory, knowledge representation, inductive logic programming, or high-performance computing. Fuzzy logic due to its proficiency in handling uncertainty has gained its importance in a variety of applications in combination with the use of data mining techniques. In this chapter we take this association a notch further by examining the parameters which allow fuzzy sets and data mining to be combined into what has come to be known as fuzzy data mining. Analyzing and understanding these critical parameters is the main purpose of this chapter, so as to acquire maximum efficiency in applying the same which impelled the authors to work extensively and find out the crucial parameters essential to the application of fuzzy data mining.

Fuzzy Sets in Data Mining and Ordinal Classification

2007 ◽  
pp. 239-254
Author(s):  
David Olson ◽  
Helen Moshkovich ◽  
Alexander Mechitov
Keyword(s):  
Data Mining ◽  
Fuzzy Sets ◽  
Ordinal Classification

Text Mining: Techniques, Applications, and Challenges

2018 ◽  
Vol 26 (04) ◽  
pp. 553-582 ◽  
Author(s):  
C. Justicia de la Torre ◽  
D. Sánchez ◽  
I. Blanco ◽  
M. J. Martín-Bautista
Keyword(s):  
Data Mining ◽  
Text Mining ◽  
Fuzzy Sets ◽  
Mining Area ◽  
Text Data ◽  
Text Data Mining ◽  
Knowledge Mining ◽  
Future Challenges

This work presents an overview of the text mining area, considering the most common techniques, and including proposals based on the application of fuzzy sets. Besides, some of the most frequent text mining applications are mentioned. We discuss the existing approaches, which we call text data mining, in relation to the recently proposed paradigm of text knowledge mining, and we conclude that both are different and complementary, in the sense that they are able to extract different knowledge pieces from text by using different reasoning mechanisms. Future challenges related to text knowledge mining are also briefly outlined.

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