Data Mining Classification: Alternative Techniques

Mar 10, 2013

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Rule-Based Classifier. Classify records by using a collection of

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Data Mining Classification: Alternative Techniques

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Data Mining Techniques for CRM

Data Mining Techniques for CRM. Seyyed Jamaleddin Pishvayi Customer Relationship Management Instructor : Dr. Taghiyare Tehran University Spring 1383. Outlines. What is Data Mining? Data Mining Motivation Data Mining Applications Applications of Data Mining in CRM Data Mining Taxonomy

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Chapter 2 Data Mining

Chapter 2 Data Mining. Faculty of Computer Science and Engineering HCM City University of Technology October- 2010. Outline. Overview of data mining Association rules Classification Regression Clustering Other Data Mining problems Applications of data mining. DATA MINING.

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CS/CMPE 536 –Data Mining

CS/CMPE 536 –Data Mining. Outline . Description. A comprehensive introduction to the concepts and techniques in data mining data mining process – its need and motivation data mining tasks and functionalities association rule mining clustering text and web mining mining sequential data

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Neural Networks in Data Mining “An Overview”

Neural Networks in Data Mining “An Overview”. Mahdi Nasereddin Ph.D. Pennsylvania State University School of Information Sciences and Technology. Agenda. Introduction Data Mining Techniques Neural Networks for Data Mining? Neural Networks Classification Neural Networks Prediction

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Data Mining

Data Mining. Introduction BIM. Objectives. Nature of Data Mining Data Mining Tools Ethics Online Survey Techniques Interpret Data. Overview. Data Mining (data or knowledge discovery)

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Data Mining: Concepts and Techniques

Data Mining: Concepts and Techniques . Chapter 1. Introduction. Motivation: Why data mining? What is data mining? Data Mining: On what kind of data? Data mining functionality Are all the patterns interesting? Major issues in data mining.

926 views • 37 slides

Data Mining Practical Machine Learning Tools and Techniques

Data Mining Practical Machine Learning Tools and Techniques Slides for Chapter 2 of Data Mining by I. H. Witten, E. Frank and M. A. Hall. Input: Concepts, instances, attributes. Terminology What’s a concept? Classification, association, clustering, numeric prediction

624 views • 35 slides

Data Mining Classification: Alternative Techniques

Data Mining Classification: Alternative Techniques. From Chapter 5 in Introduction to Data Mining by Tan, Steinbach, Kumar. Rule-Based Classifier. Classify records by using a collection of “if…then…” rules Rule: ( Condition )  y where Condition is a conjunctions of attributes

631 views • 29 slides

Introduction to Web Mining

Introduction to Web Mining. Spring 2013. What is data mining?. Data mining is extraction of useful patterns from data sources, e.g., databases, texts, web, images, etc. Patterns must be: valid, novel, potentially useful, understandable. Classic data mining tasks. Classification:

338 views • 10 slides

Analyzing Stock Quotes using Data Mining Techniques

First Presentation, Final Year Project, 2013. Analyzing Stock Quotes using Data Mining Techniques. Name of Student: To Yi Fun University Number: 2010149103. Flow of Presentation. Aim of the this classification for stock trade Theory of Classification Decision Tree making

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Data Mining Techniques for CRM

Data Mining Techniques for CRM. Lakewood Resource And Referral Center 212 2 nd Street Lakewood NJ, 08701. Outlines. What is Data Mining? Data Mining Motivation Data Mining Applications Applications of Data Mining in CRM Data Mining Taxonomy Data Mining Techniques. Data Mining.

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Visualization for Classification and Clustering Techniques

Visualization for Classification and Clustering Techniques. Marc René CSE 8331 Data Mining - Project 1. Overview. Importance of Data Visualization in the KDD Process Understanding and Trust Visualization techniques Classification Clustering Future Directions. KDD Process. Selection

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Data Mining Classification: Alternative Techniques

Data Mining Classification: Alternative Techniques. Bayesian Classifiers. Bayes Classifier. A probabilistic framework for solving classification problems Conditional Probability: Bayes theorem:. Example of Bayes Theorem. Given:

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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: Concepts and Techniques — Additional Applications and Emerging Topics —

Data Mining: Concepts and Techniques — Additional Applications and Emerging Topics —. Li Xiong Slides credits: Jiawei Han and Micheline Kamber Chris Clifton Agrawal and Srikant. Outline. Biological data mining Data mining for intrusion detection Privacy-preserving data mining.

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Pre-procesing techniques for text classification

Pre-procesing techniques for text classification. An introduction. Introduction. Text mining refers to data mining using text documents as data. Most text mining tasks use Information Retrieval (IR) methods to pre-process text documents.

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Data Mining: Concepts and Techniques

Data Mining: Concepts and Techniques. By N.Gopinath AP/CSE. Chapter 1. Introduction. Motivation: Why data mining? What is data mining? Data Mining: On what kind of data? Data mining functionality Are all the patterns interesting? Classification of data mining systems

1.07k views • 48 slides

Ch5 Data Mining Classification: Alternative Techniques

Ch5 Data Mining Classification: Alternative Techniques. Rule-Based Classifier. Classify records by using a collection of “if…then…” rules Rule: ( Condition )  y where Condition is a conjunctions of attributes y is the class label LHS : rule antecedent or condition

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An Introduction

An Introduction. Student Name: Riaz Ahmad Program: MSIT(2005-2007) Subject: Data warehouse & Data Mining. Concerned terms with my Research. KDD process Data warehouse Materialized or Indexed view Data Mining Data Mining Techniques Classification Decision Tree ID3 algorithm

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Session – I Data Mining : Concepts and Techniques

Session – I Data Mining : Concepts and Techniques. Dr. JULIE M. DAVID Asst. Professor Department of Computer Applications MES College, Marampally, Aluva. Why Data Mining?. The Explosive Growth of Data: from terabytes to petabytes Data collection and data availability

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Data Mining: Classification and Prediction

Data Mining: Classification and Prediction. What is classification? What is prediction? Issues regarding classification and prediction Classification by decision tree induction Bayesian classification Rule-based classification Classification by back propagation.

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Data Mining with Oracle using Classification and Clustering Algorithms

Data Mining with Oracle using Classification and Clustering Algorithms. Presented by Nhamo Mdzingwa Supervisor: John Ebden. Overview of Presentation. Recap of Proposal Classification of Data Mining & DM Algorithms Oracle Data Mining Data Mining Process Evaluation of Results

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