scholarly journals Data Mining in Institutional Economics Tasks

2018 ◽  
Vol 173 ◽  
pp. 03013
Author(s):  
Igor Kirilyuk ◽  
Anna Kuznetsova ◽  
Oleg Senko
Keyword(s):  
Data Mining ◽  
Institutional Economics ◽  
Single Variable ◽  
Institutional Type ◽  
Explanatory Variables ◽  
Use Of Data ◽  
Different Types ◽  
Mining Tools

The paper discusses problems associated with the use of data mining tools to study discrepancies between countries with different types of institutional matrices by variety of potential explanatory variables: climate, economic or infrastructure indicators. An approach is presented which is based on the search of statistically valid regularities describing the dependence of the institutional type on a single variable or a pair of variables. Examples of regularities are given.

Worshiping Math

2019 ◽  
pp. 65-84
Author(s):  
Gary Smith ◽  
Jay Cordes
Keyword(s):  
Data Mining ◽  
Normal Distribution ◽  
Common Sense ◽  
Fat Tails ◽  
Explanatory Variables ◽  
Good Data ◽  
Black Swans ◽  
The People ◽  
Invaluable Tool ◽  
Mining Tools

Data-mining tools, in general, tend to be mathematically sophisticated, yet often make implausible assumptions. For example, analysts often assume a normal distribution and disregard the fat tails that warn of “black swans.” Too often, the assumptions are hidden in the math and the people who use the tools are more impressed by the math than curious about the assumptions. Instead of being blinded by math, good data scientists use explanatory variables that make sense. Good data scientists use math, but do not worship it. They know that math is an invaluable tool, but it is not a substitute for common sense, wisdom, or expertise.

Violation of Homogeneity: A Methodologic Issue in the Use of Data Mining Tools

Drug Safety ◽  
2003 ◽  
Vol 26 (5) ◽  
pp. 363-364 ◽  
Author(s):  
David E Lilienfeld ◽  
Savian Nicholas ◽  
Daniel J Macneil ◽  
Olga Kurjatkin ◽  
Thomas Gelardin
Keyword(s):  
Data Mining ◽  
Use Of Data ◽  
Methodologic Issue ◽  
Mining Tools

Secure Computation for Privacy Preserving Data Mining

2011 ◽  
pp. 1747-1752 ◽  
Author(s):  
Yehuda Lindell
Keyword(s):  
Data Mining ◽  
Privacy Preserving ◽  
Secure Computation ◽  
Data Mining Algorithm ◽  
Privacy Preserving Data Mining ◽  
Public And Private ◽  
The Public ◽  
Use Of Data ◽  
To Come ◽  
Mining Tools

The increasing use of data mining tools in both the public and private sectors raises concerns regarding the potentially sensitive nature of much of the data being mined. The utility to be gained from widespread data mining seems to come into direct conflict with an individual’s need and right to privacy. Privacy preserving data mining solutions achieve the somewhat paradoxical property of enabling a data mining algorithm to use data without ever actually “seeing” it. Thus, the benefits of data mining can be enjoyed, without compromising the privacy of concerned individuals.

scholarly journals Secure Multiparty Computation for Privacy Preserving Data Mining

2011 ◽  
pp. 1005-1009 ◽  
Author(s):  
Yehida Lindell
Keyword(s):  
Data Mining ◽  
Privacy Preserving ◽  
Data Mining Algorithm ◽  
Multiparty Computation ◽  
Privacy Preserving Data Mining ◽  
Public And Private ◽  
The Public ◽  
Use Of Data ◽  
To Come ◽  
Mining Tools

The increasing use of data-mining tools in both the public and private sectors raises concerns regarding the potentially sensitive nature of much of the data being mined. The utility to be gained from widespread data mining seems to come into direct conflict with an individual’s need and right to privacy. Privacy-preserving data-mining solutions achieve the somewhat paradoxical property of enabling a data-mining algorithm to use data without ever actually seeing it. Thus, the benefits of data mining can be enjoyed without compromising the privacy of concerned individuals.

scholarly journals Machine Learning and data mining tools applied for databases of low number of records

2022 ◽  
Vol 21 (4) ◽  
pp. 346-363
Author(s):  
Hubert Anysz
Keyword(s):  
Machine Learning ◽  
Data Mining ◽  
Computational Methods ◽  
Large Datasets ◽  
Learning Tools ◽  
Data Preparation ◽  
Preparation Methods ◽  
Use Of Data ◽  
Small Set ◽  
Mining Tools

The use of data mining and machine learning tools is becoming increasingly common. Their usefulness is mainly noticeable in the case of large datasets, when information to be found or new relationships are extracted from information noise. The development of these tools means that datasets with much fewer records are being explored, usually associated with specific phenomena. This specificity most often causes the impossibility of increasing the number of cases, and that can facilitate the search for dependences in the phenomena under study. The paper discusses the features of applying the selected tools to a small set of data. Attempts have been made to present methods of data preparation, methods for calculating the performance of tools, taking into account the specifics of databases with a small number of records. The techniques selected by the author are proposed, which helped to break the deadlock in calculations, i.e., to get results much worse than expected. The need to apply methods to improve the accuracy of forecasts and the accuracy of classification was caused by a small amount of analysed data. This paper is not a review of popular methods of machine learning and data mining; nevertheless, the collected and presented material will help the reader to shorten the path to obtaining satisfactory results when using the described computational methods

scholarly journals A Most Efficient Health Care (HC) Based Algorithm for Prevention of Brain Disease Facets in Data Mining Applications

2019 ◽  
Vol 9 (2) ◽  
pp. 4572-4577
Keyword(s):  
Data Mining ◽  
Health Care ◽  
Medical Applications ◽  
Huge Amount ◽  
Healthcare Applications ◽  
Use Of Data ◽  
Storage Servers ◽  
Different Types ◽  
Development Applications ◽  
Success Percentage

Nowadays the use of data mining has been increasing rapidly in many areas like research applications, medical applications, healthcare applications, etc. The data mining applications really providing great applications for all areas due to its huge amount of data related to different types of data which was related to different types of areas in the storage servers, one of the problem with this mining applications is how to get the relevant data from the huge amount of data, many research and development applications are providing different types of solutions to retrieve the data from the mining. Once data was retrieved from the servers the users easily can solve their problems from their homes, for example, online doctor’s information systems. In the olden days when the information technology is not vastly distributed the patient doesn’t know the doctor's availability the success percentage of doctor treatment, how many doctors are available in their city, etc. This manuscript was proposing the algorithm for the healthcare system which is called query facets algorithm, which can fetches data from the server based on the query

Classification Techniques and Data Mining Tools Used in Medical Bioinformatics

2019 ◽  
pp. 105-126
Author(s):  
Satish Kumar David ◽  
Amr T. M. Saeb ◽  
Mohamed Rafiullah ◽  
Khalid Rubeaan
Keyword(s):  
Data Mining ◽  
Genomic Analysis ◽  
Resistance Pattern ◽  
Medical Applications ◽  
Classification Analysis ◽  
Classification Techniques ◽  
Data Mining Techniques ◽  
Use Of Data ◽  
Drug Resistance Pattern ◽  
Mining Tools

Increasing volumes of data with the increased availability information mandates the use of data mining techniques in order to gather useful information from the datasets. In this chapter, data mining techniques are described with a special emphasis on classification techniques as one important supervised learning technique. Bioinformatics tools in the field for medical applications especially in medical microbiology are discussed. This chapter presents WEKA software as a tool of choice to perform classification analysis for different kinds of available data. Uses of WEKA data mining tools for biological applications such as genomic analysis and for medical applications such as diabetes are discussed. Data mining offers novel tools for medical applications for infectious diseases; it can help in identifying the pathogen and analyzing the drug resistance pattern. For non-communicable diseases such as diabetes, it provides excellent data analysis options for analyzing large volumes of data from many clinical studies.

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