Worshiping Math

Author(s):  
Gary Smith ◽  
Jay Cordes

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.

2018 ◽  
Vol 173 ◽  
pp. 03013
Author(s):  
Igor Kirilyuk ◽  
Anna Kuznetsova ◽  
Oleg Senko

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.


Author(s):  
Richard C. Kittler

Abstract Analysis of manufacturing data as a tool for failure analysts often meets with roadblocks due to the complex non-linear behaviors of the relationships between failure rates and explanatory variables drawn from process history. The current work describes how the use of a comprehensive engineering database and data mining technology over-comes some of these difficulties and enables new classes of problems to be solved. The characteristics of the database design necessary for adequate data coverage and unit traceability are discussed. Data mining technology is explained and contrasted with traditional statistical approaches as well as those of expert systems, neural nets, and signature analysis. Data mining is applied to a number of common problem scenarios. Finally, future trends in data mining technology relevant to failure analysis are discussed.


Author(s):  
Bahar Dadashova ◽  
Chiara Silvestri-Dobrovolny ◽  
Jayveersinh Chauhan ◽  
Marcie Perez ◽  
Roger Bligh

Author(s):  
J. L. ÁLVAREZ-MACÍAS ◽  
J. MATA-VÁZQUEZ ◽  
J. C. RIQUELME-SANTOS

In this paper we present a new method for the application of data mining tools on the management phase of software development process. Specifically, we describe two tools, the first one based on supervised learning, and the second one on unsupervised learning. The goal of this method is to induce a set of management rules that make easy the development process to the managers. Depending on how and to what is this method applied, it will permit an a priori analysis, a monitoring of the project or a post-mortem analysis.


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