scholarly journals Predictive Data Mining Techniques for Fault Diagnosis of Electric Equipment: A Review

2020 ◽  
Vol 10 (3) ◽  
pp. 950 ◽  
Author(s):  
Arantxa Contreras-Valdes ◽  
Juan P. Amezquita-Sanchez ◽  
David Granados-Lieberman ◽  
Martin Valtierra-Rodriguez
Keyword(s):  
Data Mining ◽  
Fault Diagnosis ◽  
Scientific Field ◽  
Data Mining Techniques ◽  
Large Databases ◽  
The World ◽  
Electric Equipment ◽  
Classification And Regression ◽  
Predictive Data Mining ◽  
Year 2000

Data mining is a technological and scientific field that, over the years, has been gaining more importance in many areas, attracting scientists, developers, and researchers around the world. The reason for this enthusiasm derives from the remarkable benefits of its usefulness, such as the exploitation of large databases and the use of the information extracted from them in an intelligent way through the analysis and discovery of knowledge. This document provides a review of the predictive data mining techniques used for the diagnosis and detection of faults in electric equipment, which constitutes the pillar of any industrialized country. Starting from the year 2000 to the present, a revision of the methods used in the tasks of classification and regression for the diagnosis of electric equipment is carried out. Current research on data mining techniques is also listed and discussed according to the results obtained by different authors.

scholarly journals Market Basket Analysis for Determination of Consumer Behavior at XYZ Stores Using R Programming

2021 ◽  
Vol 3 (2) ◽  
pp. 0210206
Author(s):  
Kelik Sussolaikah
Keyword(s):  
Data Mining ◽  
Consumer Behavior ◽  
Market Basket Analysis ◽  
Process Data ◽  
Market Basket ◽  
Data Mining Techniques ◽  
Programming Tool ◽  
The World ◽  
R Programming

Data mining is one of the fields of science in the world of informatics which has an important role, especially with regard to data. There are many algorithms and methods that can be used to process data. The paper this time the author tries to conduct research on consumer behavior by using one of the data mining techniques, namely market basket analysis. This research uses the R Programming tool, where it is hoped that the research can be carried out effectively and efficiently. Based on the research conducted, it is known that there has been a significant purchase of several items that have been described as a plot. The tendency of consumers to buy several items followed by other items can be a consideration for arranging the layout of goods on the sales shelf or arranging product stock in a supermarket.

scholarly journals User’s Behaviour inside a Digital Library

Data Mining ◽  
2013 ◽  
pp. 1312-1319
Author(s):  
Marco Scarnò
Keyword(s):  
Data Mining ◽  
Digital Library ◽  
Web Mining ◽  
Dynamic Change ◽  
Web Server ◽  
Scientific Field ◽  
Data Mining Techniques ◽  
Log File ◽  
The Web ◽  
The Way

CASPUR allows many academic Italian institutions located in the Centre-South of Italy to access more than 7 million articles through a digital library platform. The behaviour of its users were analyzed by considering their “traces”, which are stored in the web server log file. Using several web mining and data mining techniques the author discovered a gradual and dynamic change in the way articles are accessed. In particular there is evidence of a journal browsing increase in comparison to the searching mode. Such phenomenon were interpreted using the idea that browsing better meets the needs of users when they want to keep abreast about the latest advances in their scientific field, in comparison to a more generic searching inside the digital library.

Bibliomining for Library Decision-Making

2011 ◽  
pp. 341-345
Author(s):  
Scott Nicholson ◽  
Jeffrey Stanton
Keyword(s):  
Data Mining ◽  
Digital Libraries ◽  
Large Data ◽  
Data Sets ◽  
Data Mining Techniques ◽  
Governmental Organizations ◽  
Non Governmental Organizations ◽  
The People ◽  
The World ◽  
Use Of The Internet

Most people think of a library as the little brick building in the heart of their community or the big brick building in the center of a campus. These notions greatly oversimplify the world of libraries, however. Most large commercial organizations have dedicated in-house library operations, as do schools, non-governmental organizations, as well as local, state, and federal governments. With the increasing use of the Internet and the World Wide Web, digital libraries have burgeoned, and these serve a huge variety of different user audiences. With this expanded view of libraries, two key insights arise. First, libraries are typically embedded within larger institutions. Corporate libraries serve their corporations, academic libraries serve their universities, and public libraries serve taxpaying communities who elect overseeing representatives. Second, libraries play a pivotal role within their institutions as repositories and providers of information resources. In the provider role, libraries represent in microcosm the intellectual and learning activities of the people who comprise the institution. This fact provides the basis for the strategic importance of library data mining: By ascertaining what users are seeking, bibliomining can reveal insights that have meaning in the context of the library’s host institution. Use of data mining to examine library data might be aptly termed bibliomining. With widespread adoption of computerized catalogs and search facilities over the past quarter century, library and information scientists have often used bibliometric methods (e.g., the discovery of patterns in authorship and citation within a field) to explore patterns in bibliographic information. During the same period, various researchers have developed and tested data mining techniques—advanced statistical and visualization methods to locate non-trivial patterns in large data sets. Bibliomining refers to the use of these bibliometric and data mining techniques to explore the enormous quantities of data generated by the typical automated library.

A comparative analysis of predictive data mining techniques

2009 ◽  
Vol 1 (2) ◽  
pp. 150 ◽  
Author(s):  
Xueping Li ◽  
Godswill Chukwugozie Nsofor ◽  
Laigang Song
Keyword(s):  
Data Mining ◽  
Comparative Analysis ◽  
Data Mining Techniques ◽  
Predictive Data Mining
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