Data Mining: Concepts and Techniques Slides for Textbook Chapter 8

Nov 30, 2012

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November 29, 2012. Data Mining: Concepts and Techniques. 2. Chapter 8. Cluster Analysis. What is Cluster Analysis?Types of Data in Cluster AnalysisA Categorization of Major Clustering MethodsPartitioning MethodsHierarchical MethodsDensity-Based MethodsGrid-Based MethodsModel-Based Clustering

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Data Mining: Concepts and Techniques Slides for Textbook Chapter 8

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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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Data Mining: Concepts and Techniques

Some of these slides are taken with some modifications from:. Data Mining: Concepts and Techniques. © Jiawei Han and Micheline Kamber Intelligent Database Systems Research Lab School of Computing Science Simon Fraser University, Canada http://www.cs.sfu.ca. Acknowledgements.

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Chapter 7: Text mining

Chapter 7: Text mining. Text mining. It refers to data mining using text documents as data. There are many special techniques for pre-processing text documents to make them suitable for mining. Most of these techniques are from the field of “Information Retrieval”. .

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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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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.

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Data Mining Practical Machine Learning Tools and Techniques

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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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Data Mining: Concepts and Techniques — Chapter 2 —

Data Mining: Concepts and Techniques — Chapter 2 —. Chapter 2: Data Preprocessing. Why preprocess the data? Descriptive data summarization Data cleaning Data integration and transformation Data reduction Discretization and concept hierarchy generation Summary. Why Data Preprocessing?.

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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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Data Mining: Concepts and Techniques — Chapter 8 — 8.2 Mining time-series data

Data Mining: Concepts and Techniques — Chapter 8 — 8.2 Mining time-series data. Chapter 8. Mining Stream, Time-Series, and Sequence Data. Mining data streams Mining time-series data Mining sequence patterns in transactional databases Mining sequence patterns in biological data.

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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

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Data Mining: Concepts and Techniques — Chapter 3 —

Data Mining: Concepts and Techniques — Chapter 3 —. Jiawei Han and Micheline Kamber. Chapter 3: Data Warehousing and OLAP Technology: An Overview. What is a data warehouse? A multi-dimensional data model Data warehouse architecture Data warehouse implementation

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Data Mining: Concepts and Techniques. Programme of Computer Science and Technology United International College. Chapter 3: Data Warehousing, and On-line Analytical Processing. Data warehouse: Basic concept Data warehouse modeling: Data cube and OLAP Data warehouse architecture

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Data Mining: Concepts and Techniques — Chapter 1 — — Introduction —

Data Mining: Concepts and Techniques — Chapter 1 — — Introduction —. Slides Adapted from Book Authors. About the course. We cover some major problems in Data Mining. The focus is on Database and algorithmic aspects Main reference:

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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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Chapter One

Chapter One. Introduction. Chapter Overview. Roles of data, information and knowledge Background of data mining What is data mining? Main data mining objectives Data mining and other related disciplines Current state of data mining Promises and challenges

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Data Mining: Concepts and Techniques (3 rd ed.) — Chapter 10 —

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Data Mining: Concepts and Techniques — Chapter 2 —

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CS 536 –Data Mining

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