Critical Parameters for Fuzzy Data Mining

2015 ◽  
pp. 1-18
Author(s):  
Sinchan Bhattacharya ◽  
Vishal Bhatnagar

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.

Author(s):  
Sinchan Bhattacharya ◽  
Vishal Bhatnagar

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.


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

2014 ◽  
Vol 556-562 ◽  
pp. 3949-3951
Author(s):  
Jian Xin Zhu

Data mining is a technique that aims to analyze and understand large source data reveal knowledge hidden in the data. It has been viewed as an important evolution in information processing. Why there have been more attentions to it from researchers or businessmen is due to the wide availability of huge amounts of data and imminent needs for turning such data into valuable information. During the past decade or over, the concepts and techniques on data mining have been presented, and some of them have been discussed in higher levels for the last few years. Data mining involves an integration of techniques from database, artificial intelligence, machine learning, statistics, knowledge engineering, object-oriented method, information retrieval, high-performance computing and visualization. Essentially, data mining is high-level analysis technology and it has a strong purpose for business profiting. Unlike OLTP applications, data mining should provide in-depth data analysis and the supports for business decisions.


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