Transcription of Data Mining Classification: Basic Concepts and Techniques
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Data Mining Classification: Basic Concepts and Techniques
Data Mining Classification: Basic Concepts and TechniquesLecture Notes for Chapter 3 Introduction to Data Mining , 2ndEditionbyTan, Steinbach, Karpatne, Kumar2/1/2021 Introduction to Data Mining , 2ndEdition1 Classification: DefinitionlGiven a collection of records (training set ) Each record is by characterized by a tuple (x,y), where x is the attribute set and y is the class label x: attribute, predictor, independent variable, input y: class, response, dependent variable, outputlTask: Learn a model that maps each attribute set x into one of the predefined class labels y2/1/2021 Introduction to Data Mining , 2ndEdition212 Examples of Classification TaskTa s kAttribute set, xClass label, yCategorizing email messagesFeatures extracted from email message header and contentspam or non-spamIdentifying tumor cellsFeatures extracted from x-rays or MRI scansmalignant or benign cellsCataloging galaxiesFeatures extracted from telescope imagesElliptical, spiral, or irregular-shaped galaxies2/1/2021 Introduction to Data Mining , 2ndEdition3 General Approach for Building Classification Model2
Techniques Lecture Notes for Chapter 3 Introduction to Data Mining, 2nd Edition by Tan, Steinbach, Karpatne, Kumar 2/1/2021 Introduction to Data Mining, 2nd Edition 1 Classification: Definition l Given a collection of records (training set ) – Each record is by characterized by a tuple (x,y), where x is the attribute set and y is the class label
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