Knowledge Acquisition of Interval Set-Valued Based on Granular Computing

2014 ◽  
Vol 543-547 ◽  
pp. 2017-2023
Author(s):  
Qing Guan ◽  
Jian He Guan
Keyword(s):  
Machine Learning ◽  
Knowledge Discovery ◽  
Set Theory ◽  
Rough Set ◽  
Granular Computing ◽  
Rough Set Theory ◽  
Fuzzy Rough Set ◽  
Decision Systems ◽  
Machine Learning Methods ◽  
Rough Theory

The technique of a new extension of fuzzy rough theory using partition of interval set-valued is proposed for granular computing during knowledge discovery in this paper. The natural intervals of attribute values in decision system to be transformed into multiple sub-interval of [0,1]are given by normalization. And some characteristics of interval set-valued of decision systems in fuzzy rough set theory are discussed. The correctness and effectiveness of the approach are shown in experiments. The approach presented in this paper can also be used as a data preprocessing step for other symbolic knowledge discovery or machine learning methods other than rough set theory.

Artificial Intelligence-Led Content Publishing, Metadata Creation, and Knowledge Discovery

2021 ◽  
pp. 198-213
Author(s):  
Usha B. Biradar ◽  
Lokanath Khamari ◽  
Jignesh Bhate
Keyword(s):  
Artificial Intelligence ◽  
Knowledge Discovery ◽  
End Users ◽  
Scholarly Publishing ◽  
Decision Systems ◽  
The Past ◽  
Track Record ◽  
The Status ◽  
Editorial Decision ◽  
Insight Into

Digital transitions have had strong headwinds in scholarly publishing for the past decade. It started with digitising content and is resting somewhere between tying up diverse content and catering to diverse end users. The goal is still to keep up with the changing landscape, and a demonstrable way of doing so is to actively participate by quickly adapting to standards. Artificial intelligence (AI) has a proven track record of helping with this and is an integral part of the solution frameworks. The chapter content includes a brief insight into some practices and workflows within scholarly publishing that stand to benefit from direct intervention of AI. These include editorial decision systems, metadata enrichments, metadata standardization, and search augmentations. The authors bring to light various developments in scholarly publishing and the status of some of the best implementations of AI techniques in aiding and upkeep of the ‘digital transformations'.

Analisis Data Pembayaran Kredit Nasabah Bank Menggunakan Metode Data Mining

2013 ◽  
Vol 4 (1) ◽  
pp. 18-27
Author(s):  
Ira Melissa ◽  
Raymond S. Oetama
Keyword(s):  
Data Mining ◽  
Knowledge Discovery ◽  
Knowledge Discovery In Database

Data mining adalah analisis atau pengamatan terhadap kumpulan data yang besar dengan tujuan untuk menemukan hubungan tak terduga dan untuk meringkas data dengan cara yang lebih mudah dimengerti dan bermanfaat bagi pemilik data. Data mining merupakan proses inti dalam Knowledge Discovery in Database (KDD). Metode data mining digunakan untuk menganalisis data pembayaran kredit peminjam pembayaran kredit. Berdasarkan pola pembayaran kredit peminjam yang dihasilkan, dapat dilihat parameter-parameter kredit yang memiliki keterkaitan dan paling berpengaruh terhadap pembayaran angsuran kredit. Kata kunci—data mining, outlier, multikolonieritas, Anova

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