Data Mining Overview. Lecture Objectives After this lecture, you should be able to: 1.Explain key data mining tasks in your own words. 2.Draw an overview.

Slides:

Advertisements

Similar presentations

1 Statistical Modeling  To develop predictive Models by using sophisticated statistical techniques on large databases.

Advertisements

1. Abstract 2 Introduction Related Work Conclusion References.

1 DATA MINING: DEFINITIONS AND DECISION TREE EXAMPLES Emily Thomas Director of Planning and Institutional Research.

1 Lecture 5: Automatic cluster detection Lecture 6: Artificial neural networks Lecture 7: Evaluation of discovered knowledge Brief introduction to lectures.

Burton D. Morgan Entrepreneurial Competition Are you the entrepreneurial type? Do you want to start your own business and be your own boss? Do you have.

Neural Networks in Data Mining “An Overview”

Chapter 5 Data mining : A Closer Look.

Computer Science Universiteit Maastricht Institute for Knowledge and Agent Technology Data mining and the knowledge discovery process Summer Course 2005.

Enterprise systems infrastructure and architecture DT211 4

© 2011 Cengage Learning. All Rights Reserved. May not be scanned, copied or duplicated, or posted to a publicly accessible website, in whole or in part.

『 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.

Dr. Awad Khalil Computer Science Department AUC

CS490D: Introduction to Data Mining Prof. Chris Clifton April 14, 2004 Fraud and Misuse Detection.

Intelligent Systems Lecture 23 Introduction to Intelligent Data Analysis (IDA). Example of system for Data Analyzing based on neural networks.

Assessment of Model Development Techniques and Evaluation Methods for Binary Classification in the Credit Industry DSI Conference Jennifer Lewis Priestley.

Data Mining Techniques As Tools for Analysis of Customer Behavior

Data Mining Chun-Hung Chou

Understanding Data Analytics and Data Mining Introduction.

Chapter 13 Genetic Algorithms. 2 Data Mining Techniques So Far… Chapter 5 – Statistics Chapter 6 – Decision Trees Chapter 7 – Neural Networks Chapter.

Dr. Russell Anderson Dr. Musa Jafar West Texas A&M University.

ANALYTICS BUSINESS INTELLIGENCE SOFTWARE STATISTICS Kreara Solutions | 9 years | 60 members | ISO 9001:2008.

COMP3503 Intro to Inductive Modeling

Year 10 Retail Business Unit 3 – Lesson 4. Lesson Objectives Will be able to evaluate the importance of the functional areas Evaluation ~ GCSE Grade A*

Mailing Campaign Model Nan Yang University of Central Florida 04/11/2008.

Lecture 9: Knowledge Discovery Systems Md. Mahbubul Alam, PhD Associate Professor Dept. of AEIS Sher-e-Bangla Agricultural University.

Data Mining By : Tung, Sze Ming ( Leo ) CS 157B. Definition A class of database application that analyze data in a database using tools which look for.

Some working definitions…. ‘Data Mining’ and ‘Knowledge Discovery in Databases’ (KDD) are used interchangeably Data mining = –the discovery of interesting,

Introduction to SQL Server Data Mining Nick Ward SQL Server & BI Product Specialist Microsoft Australia Nick Ward SQL Server & BI Product Specialist Microsoft.

Banking on Analytics Dr A S Ramasastri Director, IDRBT.

Business Intelligence and Decision Modeling Week 9 Customer Profiling Decision Trees (Part 2) CHAID CRT.

Data Mining In contrast to the traditional (reactive) DSS tools, the data mining premise is proactive. Data mining tools automatically search the data.

Loan Default Model Saed Sayad 1www.ismartsoft.com.

Jennifer Lewis Priestley Presentation of “Assessment of Evaluation Methods for Prediction and Classification of Consumer Risk in the Credit Industry” co-authored.

Becerra-Fernandez, et al. -- Knowledge Management 1/e -- © 2004 Prentice Hall Chapter 12 Discovering New Knowledge – Data Mining.

MKT 700 Business Intelligence and Decision Models Algorithms and Customer Profiling (1)

1 STAT 5814 Statistical Data Mining. 2 Use of SAS Data Mining.

Nurissaidah Ulinnuha. Introduction Student academic performance ( ) Logistic RegressionNaïve Bayessian Artificial Neural Network Student Academic.

October 2-3, 2015, İSTANBUL Boğaziçi University Prof.Dr. M.Erdal Balaban Istanbul University Faculty of Business Administration Avcılar, Istanbul - TURKEY.

MKT 700 Business Intelligence and Decision Models Week 8: Algorithms and Customer Profiling (1)

MIS2502: Data Analytics Advanced Analytics - Introduction.

MODELING RESPONSE TO DIRECT MAIL MARKETING ISQS 7342 – Dr. Zhangxi Lin by Junil Chang.

CS 445/545 Machine Learning Winter, 2014 See syllabus at

DR. SATISH NARGUNDKAR GEORGIA STATE UNIVERSITY Analytics Overview.

Data Mining Copyright KEYSOFT Solutions.

Customer Relationship Management (CRM) Chapter 4 Customer Portfolio Analysis Learning Objectives Why customer portfolio analysis is necessary for CRM implementation.

Knowledge Discovery and Data Mining 19 th Meeting Course Name: Business Intelligence Year: 2009.

Financial Data mining and Tools CSCI 4333 Presentation Group 6 Date10th November 2003.

DATA MINING It is a process of extracting interesting(non trivial, implicit, previously, unknown and useful ) information from any data repository. The.

A Decision Support Based on Data Mining in e-Banking Irina Ionita Liviu Ionita Department of Informatics University Petroleum-Gas of Ploiesti.

Applying Adaptive Software Development (ASD) Agile Modeling on Predictive Data Mining Applications: ASD-DM Methodology M. Alnoukari 1 Z.Alzoabi 2 S.Hanna.

Data Resource Management – MGMT An overview of where we are right now SQL Developer OLAP CUBE 1 Sales Cube Data Warehouse Denormalized Historical.

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.

Jeremy Kingry, eBECS | PREDICTIVE INTELLIGENCE AND WHY YOU WANT TO KNOW ABOUT IT.

How Text Mining Helps To Find Fradulent Buyers. Text analytics can help companies discover true market perceptions, but only if the analysis is done in.

11/26/2017 MKTG 403: Customer Relationship Management & Data Analytics

Decision Trees in Analytical Model Development

MIS2502: Data Analytics Advanced Analytics - Introduction

DATA MINING © Prentice Hall.

RESEARCH APPROACH.

Data Mining Techniques So Far…

Prediction as Data Mining Task

Dr. Satish Nargundkar Georgia State University

Week 11 Knowledge Discovery Systems & Data Mining :

Others Structure Prediction Clustering DATA MINING Association Rules

Prepared by: Mahmoud Rafeek Al-Farra

כריית נתונים.

Somi Jacob and Christian Bach

Data Mining Overview.

Module 14: Performing Predictive Analysis with Data Mining

Presentation transcript:

Data Mining Overview

Lecture Objectives After this lecture, you should be able to: 1.Explain key data mining tasks in your own words. 2.Draw an overview of the Data Mining Process. 3.Discuss one broad business application of data mining. 4.Explain one way to evaluate effectiveness of a Data Mining project.

Data Mining Tasks 1.Prediction / Classification 2.Segmentation 3.Association

Course Overview/Techniques Used Data Preparation Prediction/Classification Discriminant Analysis Logistic Regression Artificial Neural Networks Classification Trees (CART, CHAID) Segmentation Judgement Factor Analysis Cluster Analysis Association Market Basket Analysis Other Correlation Based techniques

Data Mining Process Source: CRISP-DM (SPSS.com website)

Application in Financial Services Product Planning Customer Acquisition Collections and Recovery Customer Manage- ment Customer Valuation Stage 1Stage 2 Stage 4 Stage 3

Measuring Effectiveness: Lift/Gains Chart Dr. Satish Nargundkar Percent of population targeted Percent of potential responders captured Targeting Random mailing

Discussion 1.Can you think of other applications? 2.What are some limitations of Data Mining? 3.What are future possibilities?