Data Mining By Minh Osborne. Overview What is data mining? What can data mining do for you? The technologies involved with data mining.

Slides:



Advertisements
Similar presentations
Introduction BIM. Objectives Nature of Data Mining Data Mining Tools Ethics Online Survey Techniques Interpret Data.
Advertisements

Final Review and Study Guide MIS2502, Spring 2011 Section 03.
1 Data Warehousing. 2 Data Warehouse A data warehouse is a huge database that stores historical data Example: Store information about all sales of products.
When to use Data Mining. Introduction An important question that should be answered before you commence any data mining project is whether data mining.
Week 9 Data Mining System (Knowledge Data Discovery)
© Prentice Hall1 DATA MINING TECHNIQUES Introductory and Advanced Topics Eamonn Keogh (some slides adapted from) Margaret Dunham Dr. M.H.Dunham, Data Mining,
Data Mining.
Data Mining By Archana Ketkar.
Copyright © 2004 Pearson Education, Inc.. Chapter 27 Data Mining Concepts.
Data Mining – Intro.
CS157A Spring 05 Data Mining Professor Sin-Min Lee.
Oracle Data Mining Ying Zhang. Agenda Data Mining Data Mining Algorithms Oracle DM Demo.
DASHBOARDS Dashboard provides the managers with exactly the information they need in the correct format at the correct time. BI systems are the foundation.
Data Mining: A Closer Look
Enterprise systems infrastructure and architecture DT211 4
Data Mining By Andrie Suherman. Agenda Introduction Major Elements Steps/ Processes Tools used for data mining Advantages and Disadvantages.
OLAM and Data Mining: Concepts and Techniques. Introduction Data explosion problem: –Automated data collection tools and mature database technology lead.
『 Data Mining 』 By Jung, hae-sun. 1.Introduction 2.Definition 3.Data Mining Applications 4.Data Mining Tasks 5. Overview of the System 6. Data Mining.
Knowledge Discovery & Data Mining process of extracting previously unknown, valid, and actionable (understandable) information from large databases Data.
McGraw-Hill/Irwin McGraw-Hill/Irwin Copyright © 2009 by The McGraw-Hill Companies, Inc. All rights reserved.
Data Mining By Jason Baltazar, Phil Cademas, Jillian Latham, Rachel Peeler & Kamila Singh.
Chapter 5: Data Mining for Business Intelligence
Data Mining Techniques
INTELLIGENT SYSTEMS BUSINESS MOTIVATION BUSINESS INTELLIGENCE M. Gams.
Ihr Logo Chapter 5 Business Intelligence: Data Warehousing, Data Acquisition, Data Mining, Business Analytics, and Visualization Turban, Aronson, and Liang.
Chapter 13 Genetic Algorithms. 2 Data Mining Techniques So Far… Chapter 5 – Statistics Chapter 6 – Decision Trees Chapter 7 – Neural Networks Chapter.
INTRODUCTION TO DATA MINING MIS2502 Data Analytics.
Datawarehouse Objectives
Fox MIS Spring 2011 Data Mining Week 9 Introduction to Data Mining.
Data Warehousing.
Ihr Logo Chapter 5 Business Intelligence: Data Warehousing, Data Acquisition, Data Mining, Business Analytics, and Visualization Turban, Aronson, and Liang.
BUSINESS ANALYTICS AND DATA VISUALIZATION
Data Mining – Intro. Course Overview Spatial Databases Temporal and Spatio-Temporal Databases Multimedia Databases Data Mining.
1 Topics about Data Warehouses What is a data warehouse? How does a data warehouse differ from a transaction processing database? What are the characteristics.
CS157B Fall 04 Introduction to Data Mining Chapter 22.3 Professor Lee Yu, Jianji (Joseph)
Data Mining In contrast to the traditional (reactive) DSS tools, the data mining premise is proactive. Data mining tools automatically search the data.
1 Categories of data Operational and very short-term decision making data Current, short-term decision making, related to financial transactions, detailed.
Chapter 5: Business Intelligence: Data Warehousing, Data Acquisition, Data Mining, Business Analytics, and Visualization DECISION SUPPORT SYSTEMS AND BUSINESS.
Information systems and management in business Chapter 8 Business Intelligence (BI)
What is Data Mining? process of finding correlations or patterns among dozens of fields in large relational databases process of finding correlations or.
Data Mining BY JEMINI ISLAM. Data Mining Outline: What is data mining? Why use data mining? How does data mining work The process of data mining Tools.
DWH-Ahsan Abdullah 1 Data Warehousing Lecture-29 Brief Intro. to Data Mining Virtual University of Pakistan Ahsan Abdullah Assoc. Prof. & Head Center.
MIS2502: Data Analytics Advanced Analytics - Introduction.
DATA MINING PREPARED BY RAJNIKANT MODI REFERENCE:DOUG ALEXANDER.
Data Mining Basics. “Copyright and Terms of Service Copyright © Texas Education Agency. The materials found on this website are copyrighted © and trademarked.
Data Mining and Decision Support
CS507 Information Systems. Lesson # 11 Online Analytical Processing.
1 Categories of data Operational and very short-term decision making data Current, short-term decision making, related to financial transactions, detailed.
Data Resource Management Agenda What types of data are stored by organizations? How are different types of data stored? What are the potential problems.
Data Mining. Overview the extraction of hidden predictive information from large databases Data mining tools predict future trends and behaviors, allowing.
Preparing for the Future with Decision Support Systems Copyright © 2001 by Harcourt, Inc. All rights reserved.
Data Mining Copyright KEYSOFT Solutions.
Chapter 2 Data, Text, and Web Mining. Data Mining Concepts and Applications  Data mining (DM) A process that uses statistical, mathematical, artificial.
Smart Web Search Agents Data Search Engines >> Information Search Agents - Traditional searching on the Web is done using one of the following three: -
Business Intelligence Overview. What is Business Intelligence? Business Intelligence is the processes, technologies, and tools that help us change data.
Introduction.  Instructor: Cengiz Örencik   Course materials:  myweb.sabanciuniv.edu/cengizo/courses.
Data Mining Introduction to data mining concepts.
Why Intelligent Data Analysis? Joost N. Kok Leiden Institute of Advanced Computer Science Universiteit Leiden.
Data Mining is the process of analyzing data and summarizing it into useful information Data Mining is usually used for extremely large sets of data It.
Data Mining: Confluence of Multiple Disciplines Data Mining Database Systems Statistics Other Disciplines Algorithm Machine Learning Visualization.
Data Mining – Intro.
DSS & Warehousing Systems
MIS2502: Data Analytics Advanced Analytics - Introduction
DATA MINING © Prentice Hall.
INTELLIGENT SYSTEMS BUSINESS MOTIVATION BUSINESS INTELLIGENCE
قاعدة البيانات Database
قاعدة البيانات Database
C.U.SHAH COLLEGE OF ENG. & TECH.
Big DATA.
Welcome! Knowledge Discovery and Data Mining
Presentation transcript:

Data Mining By Minh Osborne

Overview What is data mining? What can data mining do for you? The technologies involved with data mining.

What is Data Mining? Data Mining is a tool that businesses use to help them increase revenue by understanding their consumer. It helps find relationships and patterns in a database that were previously unknown to a business.

What can Data Mining do for you? Businesses are always looking for ways to increase profits. Cut costs Cut labor Increase productivity Understand customer

What can Data Mining…… cont’d By knowing your customer, you can better tailor goods and services to them. Targeted sales promotions. Selectively sway the right type of people. Understand consumer buying habits. What’s hot and what’s not.

What can Data Mining…… cont’d By knowing your customer and your business, you can create better business models and steer the company to successful profitability.

What it can’t do Like any tool, data mining is only useful when use correctly within the right context. It can’t produce miracle results and boost revenues overnight. Data mining allows you to analyze huge databases and from the results, you can make your own judgment on how useful it is.

Technology of Data Mining Data Mining and data warehousing Cleansing data Creating a subset database of cleansed data

Technology of Data Mining cont’d Data Mining vs OLAP OLAP – on-line analytical processing E.g. Let’s say you work at toy company and you see that more non electronic toys are being sold. You formulate a hypothesis that the reason for this is because the non electronic toys are cheaper. You query the database and analyze the numbers.

Technology of Data Mining cont’d This doesn’t explain why so you formulate another hypothesis saying that it’s the age group of the toys, there are more younger children than older children. You query the data base again and check your results. OLAP is basically used to verify hypothesis by querying the database. Data Mining is different in that is uses the data itself to uncover patterns.

Technology of Data Mining cont’d Data classification methods Statistical Algorithm Neural Networks Genetic algorithms Nearest neighbor method Rule induction Data visualization

Pitfalls of Data Mining Data mining doesn’t solve all problems. It can only give you what patterns and relationships it finds. You must analyze the outcome for yourself and produce models to see if to see if they fit in the real world.