Data Mining in Artificial Intelligence: Decision Trees

Data Mining in Artificial Intelligence: Decision Trees. Outline. Introduction: Data Flood Top-Down Decision Tree Construction Choosing the Splitting Attribute Information Gain and Gain Ratio Pruning. Trends leading to Data Flood. More data is generated:

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Data Mining, Decision Trees and Earthquake Prediction

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Data Mining and Decision Trees

Lecture 2 CS157B. Data Mining and Decision Trees. Prof. Sin-Min Lee Department of Computer Science. Evolution of Database Technology. 1960s: Data collection, database creation, IMS and network DBMS 1970s: Relational data model, relational DBMS implementation 1980s:

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Mining Date: Using Data for Decision-making

Mining Date: Using Data for Decision-making. Rob Horner, Anne Todd, Steve Newton, Bob Algozzine, Kate Algozzine. Goals. Define three roles for data use in a problem-solving model Define the key features of a problem statement

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Data Mining With Decision Trees

MSCS282 - Data Mining With Decision Trees. 2. Overview. Decision TreesRules and Language BiasConstructing Decision TreesSome AnalysesHeuristicsQuality AssessmentExtensions. MSCS282 - Data Mining With Decision Trees. 3. Goals. Explore the complete data mining processUnderstand decision trees a

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Data Mining Classification: Basic Concepts, Decision Trees, and Model Evaluation

Data Mining Classification: Basic Concepts, Decision Trees, and Model Evaluation. Lecture Notes for Chapter 3. Why Data Mining. Credit ratings/targeted marketing : Given a database of 100,000 names, which persons are the least likely to default on their credit cards?

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Data Mining Classification: Basic Concepts, Decision Trees, and Model Evaluation

Data Mining Classification: Basic Concepts, Decision Trees, and Model Evaluation. Lecture Notes for Chapter 4 Introduction to Data Mining by Tan, Steinbach, Kumar. Classification: Definition. Given a collection of records ( training set )

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MIS2502: Data Analytics Classification using Decision Trees

MIS2502: Data Analytics Classification using Decision Trees. What is classification?. Determining to what group a data element belongs Or “attributes” of that “entity” Examples Determining whether a customer should be given a loan Flagging a credit card transaction as a fraudulent charge

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Decision Support: Data Mining

Decision Support: Data Mining. Introduction. Why Mine Data? Commercial Viewpoint. Lots of data is being collected and warehoused Web data, e-commerce purchases at department/ grocery stores Bank/Credit Card transactions Computers have become cheaper and more powerful

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Mining Decision Trees from Data Streams

Mining Decision Trees from Data Streams. Thanks: Tong Suk Man Ivy HKU. Contents. Introduction: problems in mining data streams Classification of stream data VFDT algorithm Window approach CVFDT algorithm Experimental results Conclusions Future work. Data Streams. Characteristics

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Classification using Decision Trees

Classification using Decision Trees. Data Mining and Information Data Mining and Machine Learning Techniques Decision trees and C5 Applications Plan for this week. Data Mining and Information. Any result should answer a practical or theoretical question.

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Data Mining and Machine Learning Decision Trees and ID3

Data Mining and Machine Learning Decision Trees and ID3. David Corne, dwcorne@gmail.com. Decision Trees. Real world applications of DTs. See here for a list: http://www.cbcb.umd.edu/~salzberg/docs/murthy_thesis/survey/node32.html

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Data Mining, Decision Trees and Earthquake Prediction

CS157B Lecture 4. Data Mining, Decision Trees and Earthquake Prediction. Professor Sin-Min Lee. What is Data Mining?. Process of automatically finding the relationships and patterns, and extracting the meaning of enormous amount of data. Also called “knowledge discovery”. Objective.

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Data Mining using Decision Trees

Data Mining using Decision Trees. Professor J. F. Baldwin. Decision Trees from Data Base. Ex Att Att Att Concept Num Size Colour Shape Satisfied 1 med blue brick yes 2 small red wedge no 3 small red sphere yes 4 large red wedge no 5 large green pillar yes

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Data Mining – Algorithms: Decision Trees - ID3

Data Mining – Algorithms: Decision Trees - ID3. Chapter 4, Section 4.3. Common Recursive Decision Tree Induction Pseudo Code. If all instances have same classification Stop, create leaf with that classification Else Choose attribute for root

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Data Mining Classification: Basic Concepts, Decision Trees, and Model Evaluation

Data Mining Classification: Basic Concepts, Decision Trees, and Model Evaluation. Lecture Materials extracted mainly from Text “Introduction to Data Mining” of Tan, Steinbach, Kumar. Classification: Definition. Given a collection of records ( training set )

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Decision trees for stream data mining – new results

Decision trees for stream data mining – new results. Leszek Rutkowski – leszek.rutkowski@iisi.pcz.pl Lena Pietruczuk – lena.pietruczuk@iisi.pcz.pl Maciej Jaworski – maciej.jaworski@iisi.pcz.pl Piotr Duda – piotr.duda@iisi.pcz.pl. Czestochowa University of Technology

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Data Mining – Algorithms: Decision Trees - ID3

Data Mining – Algorithms: Decision Trees - ID3. Chapter 4, Section 4.3. Common Recursive Decision Tree Induction Pseudo Code. If all instances have same classification Stop, create leaf with that classification Else Choose attribute for root

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Applied Data Mining Basic Decision Trees in R

Applied Data Mining Basic Decision Trees in R. Myles Gartland Rockhurst University. To GUI or Not to GUI. CLI IDE GUI. Classification vs. Prediction. Classification predicts categorical class labels (discrete or nominal)

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Data Mining Classification: Basic Concepts, Decision Trees, and Model Evaluation

COP5577: Principles of Data Mining Fall 2009 Lecture 4 Dr. Tao Li Florida International University. Data Mining Classification: Basic Concepts, Decision Trees, and Model Evaluation. Lecture Notes for Chapter 4 Introduction to Data Mining by Tan, Steinbach, Kumar. Classification: Definition.

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