Introduction to Identification of Outliers
The main aim of this book is to develop and discuss about the problem of outlier management technique, in conjunction with the classifier, is used to the well-known issue of automatically classifying chromosomes into their biological classifications. Clinical databases have amassed enormous amounts of data about individuals and their medical problems. Detecting outliers is a critical need for data mining and machine learning. When data mining and machine learning algorithms are used to datasets that include outliers, incorrect inferences about the data are drawn. The purpose of this research is to utilise data mining methods to discover connections within a big clinical database. Relationships and trends discovered within this data set may lead to the discovery of new medical knowledge.
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Introduction to Identification of Outliers
The main aim of this book is to develop and discuss about the problem of outlier management technique, in conjunction with the classifier, is used to the well-known issue of automatically classifying chromosomes into their biological classifications. Clinical databases have amassed enormous amounts of data about individuals and their medical problems. Detecting outliers is a critical need for data mining and machine learning. When data mining and machine learning algorithms are used to datasets that include outliers, incorrect inferences about the data are drawn. The purpose of this research is to utilise data mining methods to discover connections within a big clinical database. Relationships and trends discovered within this data set may lead to the discovery of new medical knowledge.
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Introduction to Identification of Outliers

Introduction to Identification of Outliers

Introduction to Identification of Outliers

Introduction to Identification of Outliers

Overview

The main aim of this book is to develop and discuss about the problem of outlier management technique, in conjunction with the classifier, is used to the well-known issue of automatically classifying chromosomes into their biological classifications. Clinical databases have amassed enormous amounts of data about individuals and their medical problems. Detecting outliers is a critical need for data mining and machine learning. When data mining and machine learning algorithms are used to datasets that include outliers, incorrect inferences about the data are drawn. The purpose of this research is to utilise data mining methods to discover connections within a big clinical database. Relationships and trends discovered within this data set may lead to the discovery of new medical knowledge.

Product Details

ISBN-13: 9786208436896
Publisher: LAP Lambert Academic Publishing
Publication date: 03/21/2025
Pages: 140
Product dimensions: 6.00(w) x 9.00(h) x 0.33(d)
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