DATA MINING

Sep 10, 2012

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Making sense out of data. With the hard drives prices becoming inexpensive the amount of data stored in the databases by the corporations has increased dramatically.Just having the raw data in the database is of no use unless someone makes sense of the data. For example one could store a decade of

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DATA MINING

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Data Mining

Data Mining. Chris Nelson CS 157 A Fall 2007. Data Mining. New buzzword, old idea. Inferring new information from already collected data. Traditionally job of Data Analysts

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DATA MINING

DATA MINING. CS157A Swathi Rangan. A Brief History of Data Mining. The term “Data Mining” was only introduced in the 1990s. Data Mining roots are traced back along three family lines: classical statistics, artificial intelligence, and machine learning.

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Data Mining

Data Mining. Dr. Tahar Kechadi Dr. Joe Carthy. 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 Major issues in data mining.

1.37k views • 15 slides

Data Mining

Data Mining. IS698 Min Song. 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 Data Mining Task Primitives

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Data Mining: Data

What is Data?. Collection of data objects and their attributesAn attribute is a property or characteristic of an objectExamples: eye color of a person, temperature, etc.Attribute is also known as variable, field, characteristic, or featureA collection of attributes describe an objectObject is

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Data Mining

Data Mining. Lecture 1 TIES445 Data mining Nov-Dec 2007 Sami Äyrämö. Data Mining. 12-14 lectures (on weeks 44-50) Mondays 12:15-14:00 Tuesdays 10:15-12:00 NOTE: No lectures on week 47 3 x 2h demonstrations (one weeks 48-50 in a computer classroom) Final exam in January 2008

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Data Mining: Data

Data Mining: Data. Lecture Notes for Chapter 2 CSE 572 Data Mining Instructor: Jieping Ye Department of Computer Science and Engineering Arizona State University. What is Data?. Collection of data objects and their attributes An attribute is a property or characteristic of an object

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Data Mining: Proses Data Mining

Data Mining: Proses Data Mining. Romi Satria Wahon o romi@romisatriawahono.net http://romisatriawahono.net +6281586220090. Romi Satria Wahono. SD Sompok Semarang (1987) SMPN 8 Semarang (1990) SMA Taruna Nusantara , Magelang (1993)

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Data Mining: Proses Data Mining

Data Mining: Proses Data Mining. Romi Satria Wahon o romi@romisatriawahono.net http://romisatriawahono.net 0815-86220090. Romi Satria Wahono. SD Sompok Semarang (1987) SMPN 8 Semarang (1990) SMA Taruna Nusantara , Magelang (1993)

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Data Mining: Data

Data Mining: Data. Lecture Notes for Chapter 2 Introduction to Data Mining by Tan, Steinbach, Kumar. What is Data?. Collection of data objects and their attributes An attribute is a property or characteristic of an object Examples: eye color of a person, temperature, etc.

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Data Mining: P enelitian Data Mining

Data Mining: P enelitian Data Mining. Romi Satria Wahon o romi@romisatriawahono.net http://romisatriawahono.net +6281586220090. Romi Satria Wahono. SD Sompok Semarang (1987) SMPN 8 Semarang (1990) SMA Taruna Nusantara , Magelang (1993)

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Data Mining

Data Mining. Database Systems Timothy Vu. Mining.

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Data Mining

tutorial based on the book:. Data Mining. Concepts and Tehniques. by Jiawei Han and Micheline Kamber. This material was developed with financial help of the WUSA fund of Austria. 1. Introduction. What motivated data mining?. Necessity is the mother of invention.

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Data Mining: Data

Data Mining: Data. Lecture Notes for Chapter 2 Introduction to PCA (Principal Component Analysis). What is PCA?. Stands for “Principal Component Analysis” Useful technique in many applications such as face recognition, image compression, finding patterns in data of high dimension

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Data Mining

Data Mining. Lecture 2. Course Syllabus. Course topics : Introduction ( Week1-Week2 ) What is Data Mining? Data Collection and Data Management Fundamentals The Essentials of Learning The Emerging Needs for Different Data Analysis Perspectives

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Data Mining: Data

Data Mining: Data. Lecture 3 TIES445 Data mining Nov-Dec 2007 Sami Äyrämö. Data quality. GIGO – Garbage In, Garbage Out Effectiveness of DM exercise depends on the quality of data Data quality concerns individual measurements (records and fields) collections of observations

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Data-mining

Data-mining. Some useful resources. Data Mining? “Yeah, I could use a nap…”. What We’ll Discuss. Why search Who should do it Sources National Library of Medicine Includes the NLM Gateway, PubMed, ClinicalTrials.gov, MEDLINE Plus, and more Google FDA website

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Data Mining

Data Mining. extracting knowledge from a large amount of data. Useful Information (sometimes 1 bit: Y/N). More data volume = less information. Data Mining. Raw data. Information Pyramid.

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Data Mining: Data

Data Mining: Data. Lecture Notes for Chapter 2 Introduction to Data Mining by Tan, Steinbach, Kumar (edited by S. Parthasarathy 3/2007). What is Data?. Collection of data objects and their attributes An attribute is a property or characteristic of an object

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Data Mining: Data

Data Mining: Data. Introduction to Data Mining by Tan, Steinbach, Kumar. What is Data?. Collection of data objects and their attributes An attribute is a property or characteristic of an object Examples: eye color of a person, temperature, etc.

1.08k views • 68 slides

Data Mining: Data

Data Mining: Data. Lecture Notes for Chapter 2 Introduction to Data Mining by Minqi Zhou. Precap –What’s Data Mining?. Many Definitions Non-trivial extraction of implicit, previously unknown and potentially useful information from data

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Data Mining: Data

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