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Data Mining: Exploring Data

Oct 03, 2012

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Data Mining: Exploring Data

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© Tan,Steinbach, Kumar Introduction to Data Mining 4/18/2004 1 Data Mining: Exploring Data Figures for Chapter 3 Introduction to Data Mining by Tan, Steinbach, Kumar

Regression for Data Mining

Regression for Data Mining. Mgt. 2206 – Introduction to Analytics Matthew Liberatore Thomas Coghlan. Learning Objectives. To understand the application of regression analysis in data mining Linear/nonlinear Logistic (Logit) To understand the key statistical measures of fit

3.12k views • 103 slides

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.

1.54k views • 68 slides

Scalable Data Mining

autonlab.org. Scalable Data Mining. Brigham Anderson, Andrew Moore, Dan Pelleg, Alex Gray, Bob Nichols, Andy Connolly. The Auton Lab, Carnegie Mellon University www.autonlab.org. Outline. autonlab.org. Kd-trees Fast nearest-neighbor finding Fast K-means clustering

1.3k views • 104 slides

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.

2.36k views • 154 slides

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 )

1.71k views • 106 slides

Data Mining

Data Mining . Seonggyu Kim. Data Mining. Data – recorded facts Information – set of patterns or expectations. Data Mining – extraction of implicit, previously unknown, and potentially useful information from data

2.42k views • 193 slides

Data Mining Tools

Data Mining Tools. Overview &amp; Tutorial Ahmed Sameh Prince Sultan University Department of Computer Science &amp; Info Sys May 2010 (Some slides belong to IBM). Introduction Outline. Goal: Provide an overview of data mining. Define data mining Data mining vs. databases

3.97k views • 301 slides

CS 490 Sample Project Mining the Mushroom Data Set

CS 490 Sample Project Mining the Mushroom Data Set. Kirk Scott. Yellow Morels. Black Morels. This set of overheads begins with the contents of the project check-off sheet After that an example project is given. CS 490 Data Mining Project Check-Off Sheet. Student's name: _______

1.72k views • 152 slides

INTRODUCTION TO DATA MINING

INTRODUCTION TO DATA MINING. Pinakpani Pal Electronics &amp; Communication Sciences Unit Indian Statistical Institute pinak@isical.ac.in. Main Sources. Data Mining Concepts and Techniques – Jiawei Han and Micheline Kamber , 2007

1.62k views • 133 slides

Web Mining : A Bird ’ s Eye View

Web Mining : A Bird ’ s Eye View. Sanjay Kumar Madria Department of Computer Science University of Missouri-Rolla, MO 65401 madrias@umr.edu. Web Mining. Web mining - data mining techniques to automatically discover and extract information from Web documents/services (Etzioni, 1996).

2.46k views • 191 slides

CS590D: Data Mining Prof. Chris Clifton

CS590D: Data Mining Prof. Chris Clifton. January 24, 2006 Association Rules. Mining Association Rules in Large Databases. Association rule mining Algorithms for scalable mining of (single-dimensional Boolean) association rules in transactional databases

1.87k views • 166 slides

XML and Web Data

XML and Web Data. Facts about the Web. Growing fast Popular Semi-structured data Data is presented for ‘human’-processing Data is often ‘self-describing’ (including name of attributes within the data fields). Figure 17.1 A student list in HTML. Students. Vision for Web data.

1.45k views • 113 slides

Mining Complex Types of Data

Mining Complex Types of Data. 2004/10/29. Outline. 1. Generalization of Structured Data 2. Mining Spatial Databases 3. Mining Time-Series and Sequence Data 4. Mining Text Databases 5. Mining the World Wide Web. 1. Generalization of Structured Data.

1.86k views • 104 slides

Data Mining 2

Data Mining 2. Data Mining is one aspect of Database Query Processing (on the &quot;what if&quot; or pattern and trend end of Query Processing, rather than the &quot;please find&quot; or straight forward end.

1.37k views • 110 slides

DATA WAREHOUSING AND DATA MINING

DATA WAREHOUSING AND DATA MINING. Course Overview. The course: what and how 0. Introduction I. Data Warehousing II. Decision Support and OLAP III. Data Mining IV. Looking Ahead Demos and Labs. 0. Introduction.

3.15k views • 169 slides

Data Mining using Fractals and Power laws

Data Mining using Fractals and Power laws. Christos Faloutsos Carnegie Mellon University. Thank you!. Guozhu Dong Xuemin Lin Yun Yang Jeffrey Xu Yu Carol. Thanks to. Deepayan Chakrabarti (CMU/Yahoo) Michalis Faloutsos (UCR) George Siganos (UCR). Overview.

1.29k views • 118 slides

CS590D: Data Mining Chris Clifton

CS590D: Data Mining Chris Clifton. January 14, 2006 Data Preparation. Data Preprocessing. Why preprocess the data? Data cleaning Data integration and transformation Data reduction Discretization and concept hierarchy generation Summary. Why Data Preprocessing?.

1.78k views • 129 slides

DATA MINING LECTURE 4

DATA MINING LECTURE 4. Frequent Itemsets , Association Rules Evaluation Alternative Algorithms. RECAP. Mining Frequent Itemsets. Itemset A collection of one or more items Example: {Milk, Bread, Diaper} k- itemset An itemset that contains k items Support ( )

1.9k views • 150 slides

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