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.

scholarly journals PREDIKSI HASIL UJIAN NASIONAL SISWA MENENGAH ATAS : PENDEKATAN DATA MINING

Factor M ◽  
2020 ◽  
Vol 3 (1) ◽  
Author(s):  
Ummiy Fauziah Laili
Keyword(s):  
Data Mining ◽  
Fuzzy Data ◽  
Neuro Fuzzy ◽  
Bahasa Indonesia

Pemerintah Indonesia menggunakan Nilai Ujian Nasional (NUN) digunakan sebagai standar nasional untuk mengukur keberhasilan siswa. Dengan menggunakan NUN  sebagai parameter untuk mengukur tingkat keberhasilan siswa akan ada resiko yang sangat besar jika siswa gagal dalam menjalani Ujian Nasional (UN), oleh karena itu dibutuhkan sebuah model untuk melakukan prediksi terhadap NUN siswa sehingga dapat dilakukan usaha pencegahan terhadap gagalnya siswa dalam UN. Pada paper ini diusulkan sebuah metode untuk melakukan prediksi terhadap NUN siswa dengan menggunakan neuro fuzzy. Data yang digunakan dalam penelitian ini adalah nilai rapor siswa, dan nilai IQ siswa. Pada penelitian ini digunakan tiga mata pelajaran sebagai subjek penelitian yaitu Matematika, Bahasa Indonesia dan Bahasa Inggris.Berdasarkan hasil pengujian yang dilakukan menunjukkan bahwa metode neuro fuzzy dengan menggunakan data nilai dan data IQmemberikan akurasi prediksi terbaik pada mata pelajaran matematika sebesar 74%.

Finding Active Membership Functions for Genetic-Fuzzy Data Mining

2015 ◽  
Vol 14 (06) ◽  
pp. 1215-1242 ◽  
Author(s):  
Chun-Hao Chen ◽  
Tzung-Pei Hong ◽  
Yeong-Chyi Lee ◽  
Vincent S. Tseng
Keyword(s):  
Data Mining ◽  
Association Rules ◽  
Fuzzy Data ◽  
Membership Functions ◽  
Linguistic Terms ◽  
Fuzzy Association Rules ◽  
Fitness Functions ◽  
The Given ◽  
Binary Strings ◽  
Active Membership

Since transactions may contain quantitative values, many approaches have been proposed to derive membership functions for mining fuzzy association rules using genetic algorithms (GAs), a process known as genetic-fuzzy data mining. However, existing approaches assume that the number of linguistic terms is predefined. Thus, this study proposes a genetic-fuzzy mining approach for extracting an appropriate number of linguistic terms and their membership functions used in fuzzy data mining for the given items. The proposed algorithm adjusts membership functions using GAs and then uses them to fuzzify the quantitative transactions. Each individual in the population represents a possible set of membership functions for the items and is divided into two parts, control genes (CGs) and parametric genes (PGs). CGs are encoded into binary strings and used to determine whether membership functions are active. Each set of membership functions for an item is encoded as PGs with real-number schema. In addition, seven fitness functions are proposed, each of which is used to evaluate the goodness of the obtained membership functions and used as the evolutionary criteria in GA. After the GA process terminates, a better set of association rules with a suitable set of membership functions is obtained. Experiments are made to show the effectiveness of the proposed approach.

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