# Answer Intro To Data Mining

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University of the Cumberlands
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Introduction to Data Mining Assignment
Author's Name
Institutional Affiliation
Course Number and Name
Instructor's Name and Title
Assignment Due Date

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Concept of False Discovery in Data Questions
What is a false discovery rate?
A false discovery rate is an approach of envisaging category I fallacies in null
hypothesis testing when carrying out various comparisons (Naouma & Pataky, 2019).
Can a false discovery rate be completely avoided? Explain.
According to Naouma & Pataky (2019), false discovery rate cannot be avoided because
it enhances correctness for rare events that deceptively seem significant, especially in high-
yield experiments.
What was the outcome of the results of the use case?
The outcome of the use case results is identification, analyze, and grouping the various
types of datasets to be used (Naouma & Pataky, 2019).
Week’s Questions in Essay Format
Data and classifying data impact data mining such that it helps to estimate the amount
of data an individual can achieve from one irregular provided with various random variables.
Hence can measure the uncertainty of a particular variable, thus producing the correct possible
value required in the datasets or by the researchers (Tan et al., 2016). Moreover, there is a
clarification of undefined variables in the datasets hence saving most of the time which could
have been taken when analyzing various datasets. It also helps model the powerful nonlinear
associations between the unrestrained and dependent variables of different groups of datasets.
Moreover, it is applied for mapping the information in the characteristics space, and the kernel
matrix outlines the similarity computations between the different pairs of inspections.
Therefore it helps keeps in touch with nonlinear solutions in the preliminary data spaces in the
activity being carried on.

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1 Introduction to Data Mining Assignment Author's Name Institutional Affiliation Course Number and Name Instructor's Name and Title Assignment Due Date 2 Concept of False Discovery in Data Questions What is a false discovery rate? A false discovery rate is an approach of envisaging category I fallacies in null hypothesis testing when carrying out various comparisons (Naouma & Pataky, 2019). Can a false discovery rate be completely avoided? Explain. According to Naouma & Pataky (2019), false discovery rate cannot be avoided because it enhances correctness for rare events that deceptively seem significant, especially in highyield experiments. What was the outcome of the results of the use case? The outcome of the use case results is identification, analyze, and grouping the various types of datasets to be used (Naouma & Pataky, 2019). Week’s Questions in Essay Format Data and classifying data impact data mining such that it helps to estimate the amount of data an individual can achieve from one irregular provided with various random variables. Hence can measure the uncertainty of a particular variable, thus producing the correct possible value required in the datasets or by the researchers (Tan et al., 2016). Moreover, there is a clarification of undefined variables in the datasets hence saving most of the time which could have been taken when analyzing various datasets. It also helps model the powerful nonlinear associations between the unrestrained and dependent vari ...
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