Data Mining. Dr. Saed Sayad. University of Toronto

Save this PDF as:
 WORD  PNG  TXT  JPG

Size: px
Start display at page:

Download "Data Mining. Dr. Saed Sayad. University of Toronto 2010 saed.sayad@utoronto.ca. http://chem-eng.utoronto.ca/~datamining/"

Transcription

1 Data Mining Dr. Saed Sayad University of Toronto

2 Data Mining Data mining is about explaining the past and predicting the future by means of data analysis. 2

3 Data Mining Statistics AI & Machine Learning Data Mining Database & DW 3

4 Data Mining Applications CRM Banking Credit Scoring Direct Marketing/ Fundraising Fraud Detection Retail Insurance Telecom Manufacturing Science Health care/ HR Medical/ Pharma Government applications Other e-commerce Biotech/Genomics Web Travel/Hospitality Security / Anti-terrorism Junk / Anti-spam Investment / Stocks Entertainment/ Music Gambling Source: KDnuggets.com

5 Data mining activity in 2007 compare to 2006 somewhat lower 4% much lower 5% much higher 20% about the same 41% somewhat higher 30% Source: KDnuggets.com 5

6 Data Mining Steps 1 Problem Definition 2 Data Preparation 3 Data Exploration 4 Modeling 5 Evaluation 6 Deployment 6

7 CRISP-DM Process Model CRoss-Industry Standard Process for Data Mining Source: 7

8 1. Problem Definition Understanding the project objectives and requirements from a business perspective and then converting this knowledge into a data mining problem definition with a preliminary plan designed to achieve the objectives. Source: 8

9 2. Data Preparation Data DSN Data Text ETL Modeling Data 9

10 3. Data Exploration Data Exploration Univariate Analysis Bivariate Analysis Average, StDev, Min, Max,... Bar, Line, Pie,... Charts Correlation Z test,... Combination Charts 10

11 Data Exploration - Univariate 11

12 Data Exploration - Bivariate 12

13 4. Modeling Classification Regression Clustering Association Bayesian Linear Regression Hierarchical A Priori Decision Tree Robust Regression K-Means Logistic Regression Neural Network SVM 13

14 Data Mining: Classification & Regression Frequency Table Covariance Matrix Similarity Functions Neural Networks Others OneR Linear Regression KNN Perceptron SVM Bayesian LDA (Z Score) Back Propagation GA Decision Tree PCA/PCR RBF Markov Chains Logistic Regression HMM Robust Regression Scalable Methods 14

15 Modeling - Classification Age f Responder e.g., Y or N 15

16 Modeling - Regression Age f Amount Purchased e.g., $

17 Modeling - Clustering Income Age 17

18 Association Rules Market Basket Analysis 18

19 5. Evaluation Charts Stats Gain Chart Lift Chart K-S Chart Confusion Matrix Mean Square Error Variables Contribution 19

20 Predicted Negative Predicted Positive Evaluation - Confusion Matrix Positive Cases True Positive Negative Cases False Positive CM False Negative True Negative 20

21 Evaluation Gain Chart Responder% 100% 45% 10% Population% 10% 50% 100% 21

22 6. Deployment SQL VB JAVA HTML 22

23 Data Mining Team Modeler Domain Expert DBA Analyst 23

24 Data Mining Software Vendors SAS SPSS Data Mining KXEN Angoss KNIME 24

25 Case Study

CONTENTS PREFACE 1 INTRODUCTION 1 2 DATA VISUALIZATION 19

CONTENTS PREFACE 1 INTRODUCTION 1 2 DATA VISUALIZATION 19 PREFACE xi 1 INTRODUCTION 1 1.1 Overview 1 1.2 Definition 1 1.3 Preparation 2 1.3.1 Overview 2 1.3.2 Accessing Tabular Data 3 1.3.3 Accessing Unstructured Data 3 1.3.4 Understanding the Variables and Observations

More information

Azure Machine Learning, SQL Data Mining and R

Azure Machine Learning, SQL Data Mining and R Azure Machine Learning, SQL Data Mining and R Day-by-day Agenda Prerequisites No formal prerequisites. Basic knowledge of SQL Server Data Tools, Excel and any analytical experience helps. Best of all:

More information

Grow Revenues and Reduce Risk with Powerful Analytics Software

Grow Revenues and Reduce Risk with Powerful Analytics Software Grow Revenues and Reduce Risk with Powerful Analytics Software Overview Gaining knowledge through data selection, data exploration, model creation and predictive action is the key to increasing revenues,

More information

An Introduction to Data Mining

An Introduction to Data Mining An Introduction to Intel Beijing wei.heng@intel.com January 17, 2014 Outline 1 DW Overview What is Notable Application of Conference, Software and Applications Major Process in 2 Major Tasks in Detail

More information

KnowledgeSTUDIO HIGH-PERFORMANCE PREDICTIVE ANALYTICS USING ADVANCED MODELING TECHNIQUES

KnowledgeSTUDIO HIGH-PERFORMANCE PREDICTIVE ANALYTICS USING ADVANCED MODELING TECHNIQUES HIGH-PERFORMANCE PREDICTIVE ANALYTICS USING ADVANCED MODELING TECHNIQUES Translating data into business value requires the right data mining and modeling techniques which uncover important patterns within

More information

Maximierung des Geschäftserfolgs durch SAP Predictive Analytics. Andreas Forster, May 2014

Maximierung des Geschäftserfolgs durch SAP Predictive Analytics. Andreas Forster, May 2014 Maximierung des Geschäftserfolgs durch SAP Predictive Analytics Andreas Forster, May 2014 Legal Disclaimer The information in this presentation is confidential and proprietary to SAP and may not be disclosed

More information

WebFOCUS RStat. RStat. Predict the Future and Make Effective Decisions Today. WebFOCUS RStat

WebFOCUS RStat. RStat. Predict the Future and Make Effective Decisions Today. WebFOCUS RStat Information Builders enables agile information solutions with business intelligence (BI) and integration technologies. WebFOCUS the most widely utilized business intelligence platform connects to any enterprise

More information

Practical Data Science with Azure Machine Learning, SQL Data Mining, and R

Practical Data Science with Azure Machine Learning, SQL Data Mining, and R Practical Data Science with Azure Machine Learning, SQL Data Mining, and R Overview This 4-day class is the first of the two data science courses taught by Rafal Lukawiecki. Some of the topics will be

More information

THE COMPARISON OF DATA MINING TOOLS

THE COMPARISON OF DATA MINING TOOLS T.C. İSTANBUL KÜLTÜR UNIVERSITY THE COMPARISON OF DATA MINING TOOLS Data Warehouses and Data Mining Yrd.Doç.Dr. Ayça ÇAKMAK PEHLİVANLI Department of Computer Engineering İstanbul Kültür University submitted

More information

Make Better Decisions Through Predictive Intelligence

Make Better Decisions Through Predictive Intelligence IBM SPSS Modeler Professional Make Better Decisions Through Predictive Intelligence Highlights Easily access, prepare and model structured data with this intuitive, visual data mining workbench Rapidly

More information

Data Mining Applications in Higher Education

Data Mining Applications in Higher Education Executive report Data Mining Applications in Higher Education Jing Luan, PhD Chief Planning and Research Officer, Cabrillo College Founder, Knowledge Discovery Laboratories Table of contents Introduction..............................................................2

More information

How to use Big Data in Industry 4.0 implementations. LAURI ILISON, PhD Head of Big Data and Machine Learning

How to use Big Data in Industry 4.0 implementations. LAURI ILISON, PhD Head of Big Data and Machine Learning How to use Big Data in Industry 4.0 implementations LAURI ILISON, PhD Head of Big Data and Machine Learning Big Data definition? Big Data is about structured vs unstructured data Big Data is about Volume

More information

SAP Predictive Analytics: An Overview and Roadmap. Charles Gadalla, SAP @cgadalla SESSION CODE: 603

SAP Predictive Analytics: An Overview and Roadmap. Charles Gadalla, SAP @cgadalla SESSION CODE: 603 SAP Predictive Analytics: An Overview and Roadmap Charles Gadalla, SAP @cgadalla SESSION CODE: 603 Advanced Analytics SAP Vision Embed Smart Agile Analytics into Decision Processes to Deliver Business

More information

Make Better Decisions Through Predictive Intelligence

Make Better Decisions Through Predictive Intelligence IBM SPSS Modeler Professional Make Better Decisions Through Predictive Intelligence Highlights Easily access, prepare and model structured data with this intuitive, visual data mining workbench Expand

More information

How to Optimize Your Data Mining Environment

How to Optimize Your Data Mining Environment WHITEPAPER How to Optimize Your Data Mining Environment For Better Business Intelligence Data mining is the process of applying business intelligence software tools to business data in order to create

More information

DATA MINING TECHNIQUES AND APPLICATIONS

DATA MINING TECHNIQUES AND APPLICATIONS DATA MINING TECHNIQUES AND APPLICATIONS Mrs. Bharati M. Ramageri, Lecturer Modern Institute of Information Technology and Research, Department of Computer Application, Yamunanagar, Nigdi Pune, Maharashtra,

More information

HADOOP IN ENTERPRISE FUTURE-PROOF YOUR BIG DATA INVESTMENTS WITH CASCADING. Supreet Oberoi Nov. 4-6, 2014 Big Data Expo Santa Clara

HADOOP IN ENTERPRISE FUTURE-PROOF YOUR BIG DATA INVESTMENTS WITH CASCADING. Supreet Oberoi Nov. 4-6, 2014 Big Data Expo Santa Clara DRIVING INNOVATION THROUGH DATA HADOOP IN ENTERPRISE FUTURE-PROOF YOUR BIG DATA INVESTMENTS WITH CASCADING Supreet Oberoi Nov. 4-6, 2014 Big Data Expo Santa Clara ABOUT ME I am a Data Engineer, not a Data

More information

Data Mining + Business Intelligence. Integration, Design and Implementation

Data Mining + Business Intelligence. Integration, Design and Implementation Data Mining + Business Intelligence Integration, Design and Implementation ABOUT ME Vijay Kotu Data, Business, Technology, Statistics BUSINESS INTELLIGENCE - Result Making data accessible Wider distribution

More information

Model Deployment. Dr. Saed Sayad. University of Toronto 2010 saed.sayad@utoronto.ca. http://chem-eng.utoronto.ca/~datamining/

Model Deployment. Dr. Saed Sayad. University of Toronto 2010 saed.sayad@utoronto.ca. http://chem-eng.utoronto.ca/~datamining/ Model Deployment Dr. Saed Sayad University of Toronto 2010 saed.sayad@utoronto.ca http://chem-eng.utoronto.ca/~datamining/ 1 Model Deployment Creation of the model is generally not the end of the project.

More information

Chapter 12 Discovering New Knowledge Data Mining

Chapter 12 Discovering New Knowledge Data Mining Chapter 12 Discovering New Knowledge Data Mining Becerra-Fernandez, et al. -- Knowledge Management 1/e -- 2004 Prentice Hall Additional material 2007 Dekai Wu Chapter Objectives Introduce the student to

More information

IBM SPSS Modeler Professional

IBM SPSS Modeler Professional IBM SPSS Modeler Professional Make better decisions through predictive intelligence Highlights Create more effective strategies by evaluating trends and likely outcomes. Easily access, prepare and model

More information

Data Mining and Statistics for Decision Making. Wiley Series in Computational Statistics

Data Mining and Statistics for Decision Making. Wiley Series in Computational Statistics Brochure More information from http://www.researchandmarkets.com/reports/2171080/ Data Mining and Statistics for Decision Making. Wiley Series in Computational Statistics Description: Data Mining and Statistics

More information

Principles of Data Mining by Hand&Mannila&Smyth

Principles of Data Mining by Hand&Mannila&Smyth Principles of Data Mining by Hand&Mannila&Smyth Slides for Textbook Ari Visa,, Institute of Signal Processing Tampere University of Technology October 4, 2010 Data Mining: Concepts and Techniques 1 Differences

More information

Data Science with R. Introducing Data Mining with Rattle and R. Graham.Williams@togaware.com

Data Science with R. Introducing Data Mining with Rattle and R. Graham.Williams@togaware.com http: // togaware. com Copyright 2013, Graham.Williams@togaware.com 1/35 Data Science with R Introducing Data Mining with Rattle and R Graham.Williams@togaware.com Senior Director and Chief Data Miner,

More information

Data Mining Algorithms Part 1. Dejan Sarka

Data Mining Algorithms Part 1. Dejan Sarka Data Mining Algorithms Part 1 Dejan Sarka Join the conversation on Twitter: @DevWeek #DW2015 Instructor Bio Dejan Sarka (dsarka@solidq.com) 30 years of experience SQL Server MVP, MCT, 13 books 7+ courses

More information

ANALYTICS CENTER LEARNING PROGRAM

ANALYTICS CENTER LEARNING PROGRAM Overview of Curriculum ANALYTICS CENTER LEARNING PROGRAM The following courses are offered by Analytics Center as part of its learning program: Course Duration Prerequisites 1- Math and Theory 101 - Fundamentals

More information

Business Intelligence. Data Mining and Optimization for Decision Making

Business Intelligence. Data Mining and Optimization for Decision Making Brochure More information from http://www.researchandmarkets.com/reports/2325743/ Business Intelligence. Data Mining and Optimization for Decision Making Description: Business intelligence is a broad category

More information

IBM SPSS Modeler Professional

IBM SPSS Modeler Professional IBM SPSS Modeler Professional Make better decisions through predictive intelligence Highlights Create more effective strategies by evaluating trends and likely outcomes. Easily access, prepare and model

More information

Performing a data mining tool evaluation

Performing a data mining tool evaluation Performing a data mining tool evaluation Start with a framework for your evaluation Data mining helps you make better decisions that lead to significant and concrete results, such as increased revenue

More information

Silvermine House Steenberg Office Park, Tokai 7945 Cape Town, South Africa Telephone: +27 21 702 4666 www.spss-sa.com

Silvermine House Steenberg Office Park, Tokai 7945 Cape Town, South Africa Telephone: +27 21 702 4666 www.spss-sa.com SPSS-SA Silvermine House Steenberg Office Park, Tokai 7945 Cape Town, South Africa Telephone: +27 21 702 4666 www.spss-sa.com SPSS-SA Training Brochure 2009 TABLE OF CONTENTS 1 SPSS TRAINING COURSES FOCUSING

More information

Advanced In-Database Analytics

Advanced In-Database Analytics Advanced In-Database Analytics Tallinn, Sept. 25th, 2012 Mikko-Pekka Bertling, BDM Greenplum EMEA 1 That sounds complicated? 2 Who can tell me how best to solve this 3 What are the main mathematical functions??

More information

www.rese arch2systems. com Predictive Analytics Redefining Success through Analytics

www.rese arch2systems. com Predictive Analytics Redefining Success through Analytics www.rese archsystems. com Predictive Analytics Redefining Success through Analytics ResearchSystems Driving Actions through Analytics At Research Systems, we pride ourselves in driving actions through

More information

Data Mining Solutions for the Business Environment

Data Mining Solutions for the Business Environment Database Systems Journal vol. IV, no. 4/2013 21 Data Mining Solutions for the Business Environment Ruxandra PETRE University of Economic Studies, Bucharest, Romania ruxandra_stefania.petre@yahoo.com Over

More information

KNIME UGM 2014 Partner Session

KNIME UGM 2014 Partner Session KNIME UGM 2014 Partner Session DYMATRIX Stefan Weingaertner DYMATRIX CONSULTING GROUP 1 Agenda 1 Company Introduction 2 DYMATRIX Customer Intelligence Offering 3 PMML2SQL / PMML2SAS Converter 4 Uplift

More information

Machine Learning Capacity and Performance Analysis and R

Machine Learning Capacity and Performance Analysis and R Machine Learning and R May 3, 11 30 25 15 10 5 25 15 10 5 30 25 15 10 5 0 2 4 6 8 101214161822 0 2 4 6 8 101214161822 0 2 4 6 8 101214161822 100 80 60 40 100 80 60 40 100 80 60 40 30 25 15 10 5 25 15 10

More information

CRISP - DM. Data Mining Process. Process Standardization. Why Should There be a Standard Process? Cross-Industry Standard Process for Data Mining

CRISP - DM. Data Mining Process. Process Standardization. Why Should There be a Standard Process? Cross-Industry Standard Process for Data Mining Mining Process CRISP - DM Cross-Industry Standard Process for Mining (CRISP-DM) European Community funded effort to develop framework for data mining tasks Goals: Cross-Industry Standard Process for Mining

More information

New Work Item for ISO 3534-5 Predictive Analytics (Initial Notes and Thoughts) Introduction

New Work Item for ISO 3534-5 Predictive Analytics (Initial Notes and Thoughts) Introduction Introduction New Work Item for ISO 3534-5 Predictive Analytics (Initial Notes and Thoughts) Predictive analytics encompasses the body of statistical knowledge supporting the analysis of massive data sets.

More information

Name: Srinivasan Govindaraj Title: Big Data Predictive Analytics

Name: Srinivasan Govindaraj Title: Big Data Predictive Analytics Name: Srinivasan Govindaraj Title: Big Data Predictive Analytics Please note the following IBM s statements regarding its plans, directions, and intent are subject to change or withdrawal without notice

More information

INTERNATIONAL MASTER IN BUSINESS ANALYTICS AND BIG DATA

INTERNATIONAL MASTER IN BUSINESS ANALYTICS AND BIG DATA POLITECNICO DI MILANO GRADUATE SCHOOL OF BUSINESS BABD INTERNATIONAL MASTER IN BUSINESS ANALYTICS AND BIG DATA Courses Description A JOINT PROGRAM WITH POLITECNICO DI MILANO SCHOOL OF MANAGEMENT PRE-COURSES

More information

IBM SPSS Modeler 15 In-Database Mining Guide

IBM SPSS Modeler 15 In-Database Mining Guide IBM SPSS Modeler 15 In-Database Mining Guide Note: Before using this information and the product it supports, read the general information under Notices on p. 217. This edition applies to IBM SPSS Modeler

More information

Certificate Program in Applied Big Data Analytics in Dubai. A Collaborative Program offered by INSOFE and Synergy-BI

Certificate Program in Applied Big Data Analytics in Dubai. A Collaborative Program offered by INSOFE and Synergy-BI Certificate Program in Applied Big Data Analytics in Dubai A Collaborative Program offered by INSOFE and Synergy-BI Program Overview Today s manager needs to be extremely data savvy. They need to work

More information

Index Contents Page No. Introduction . Data Mining & Knowledge Discovery

Index Contents Page No. Introduction . Data Mining & Knowledge Discovery Index Contents Page No. 1. Introduction 1 1.1 Related Research 2 1.2 Objective of Research Work 3 1.3 Why Data Mining is Important 3 1.4 Research Methodology 4 1.5 Research Hypothesis 4 1.6 Scope 5 2.

More information

Business Analytics and Data Mining for CRM Business Analytics and Data Mining for CRM: Jumpstart workshop

Business Analytics and Data Mining for CRM Business Analytics and Data Mining for CRM: Jumpstart workshop : Jumpstart workshop Date and Place: Bangalore, Sep 1 st (Sat) and 2 nd (Sun) 2012 Registration Link: http://compegence.com/open-programs.php http://compegence.com/workshop-analytics-for-crm.php Audience:

More information

Operationalise Predictive Analytics

Operationalise Predictive Analytics Operationalise Predictive Analytics Publish SPSS, Excel and R reports online Predict online using SPSS and R models Access models and reports via Android app Organise people and content into projects Monitor

More information

Product recommendations and promotions (couponing and discounts) Cross-sell and Upsell strategies

Product recommendations and promotions (couponing and discounts) Cross-sell and Upsell strategies WHITEPAPER Today, leading companies are looking to improve business performance via faster, better decision making by applying advanced predictive modeling to their vast and growing volumes of data. Business

More information

DATA ANALYTICS USING R

DATA ANALYTICS USING R DATA ANALYTICS USING R Duration: 90 Hours Intended audience and scope: The course is targeted at fresh engineers, practicing engineers and scientists who are interested in learning and understanding data

More information

Improve Model Accuracy with Unstructured Data

Improve Model Accuracy with Unstructured Data IBM SPSS Modeler Premium Improve Model Accuracy with Unstructured Data Highlights Easily access, prepare and integrate structured data and text, Web and survey data Support the entire data mining process

More information

A fast, powerful data mining workbench designed for small to midsize organizations

A fast, powerful data mining workbench designed for small to midsize organizations FACT SHEET SAS Desktop Data Mining for Midsize Business A fast, powerful data mining workbench designed for small to midsize organizations What does SAS Desktop Data Mining for Midsize Business do? Business

More information

Session 10 : E-business models, Big Data, Data Mining, Cloud Computing

Session 10 : E-business models, Big Data, Data Mining, Cloud Computing INFORMATION STRATEGY Session 10 : E-business models, Big Data, Data Mining, Cloud Computing Tharaka Tennekoon B.Sc (Hons) Computing, MBA (PIM - USJ) POST GRADUATE DIPLOMA IN BUSINESS AND FINANCE 2014 Internet

More information

Hexaware E-book on Predictive Analytics

Hexaware E-book on Predictive Analytics Hexaware E-book on Predictive Analytics Business Intelligence & Analytics Actionable Intelligence Enabled Published on : Feb 7, 2012 Hexaware E-book on Predictive Analytics What is Data mining? Data mining,

More information

R Tools Evaluation. A review by Analytics @ Global BI / Local & Regional Capabilities. Telefónica CCDO May 2015

R Tools Evaluation. A review by Analytics @ Global BI / Local & Regional Capabilities. Telefónica CCDO May 2015 R Tools Evaluation A review by Analytics @ Global BI / Local & Regional Capabilities Telefónica CCDO May 2015 R Features What is? Most widely used data analysis software Used by 2M+ data scientists, statisticians

More information

Predictive Analytics Powered by SAP HANA. Cary Bourgeois Principal Solution Advisor Platform and Analytics

Predictive Analytics Powered by SAP HANA. Cary Bourgeois Principal Solution Advisor Platform and Analytics Predictive Analytics Powered by SAP HANA Cary Bourgeois Principal Solution Advisor Platform and Analytics Agenda Introduction to Predictive Analytics Key capabilities of SAP HANA for in-memory predictive

More information

Machine learning for algo trading

Machine learning for algo trading Machine learning for algo trading An introduction for nonmathematicians Dr. Aly Kassam Overview High level introduction to machine learning A machine learning bestiary What has all this got to do with

More information

Predictive Modeling and Big Data

Predictive Modeling and Big Data Predictive Modeling and Presented by Eileen Burns, FSA, MAAA Milliman Agenda Current uses of predictive modeling in the life insurance industry Potential applications of 2 1 June 16, 2014 [Enter presentation

More information

How to Get More Value from Your Survey Data

How to Get More Value from Your Survey Data Technical report How to Get More Value from Your Survey Data Discover four advanced analysis techniques that make survey research more effective Table of contents Introduction..............................................................2

More information

Building In-Database Predictive Scoring Model: Check Fraud Detection Case Study

Building In-Database Predictive Scoring Model: Check Fraud Detection Case Study Building In-Database Predictive Scoring Model: Check Fraud Detection Case Study Jay Zhou, Ph.D. Business Data Miners, LLC 978-726-3182 jzhou@businessdataminers.com Web Site: www.businessdataminers.com

More information

BEHAVIOR BASED CREDIT CARD FRAUD DETECTION USING SUPPORT VECTOR MACHINES

BEHAVIOR BASED CREDIT CARD FRAUD DETECTION USING SUPPORT VECTOR MACHINES BEHAVIOR BASED CREDIT CARD FRAUD DETECTION USING SUPPORT VECTOR MACHINES 123 CHAPTER 7 BEHAVIOR BASED CREDIT CARD FRAUD DETECTION USING SUPPORT VECTOR MACHINES 7.1 Introduction Even though using SVM presents

More information

Achieve Better Insight and Prediction with Data Mining

Achieve Better Insight and Prediction with Data Mining Clementine 12.0 Specifications Achieve Better Insight and Prediction with Data Mining Data mining provides organizations with a clearer view of current conditions and deeper insight into future events.

More information

1 Choosing the right data mining techniques for the job (8 minutes,

1 Choosing the right data mining techniques for the job (8 minutes, CS490D Spring 2004 Final Solutions, May 3, 2004 Prof. Chris Clifton Time will be tight. If you spend more than the recommended time on any question, go on to the next one. If you can t answer it in the

More information

What is Data Mining? Data Mining (Knowledge discovery in database) Data mining: Basic steps. Mining tasks. Classification: YES, NO

What is Data Mining? Data Mining (Knowledge discovery in database) Data mining: Basic steps. Mining tasks. Classification: YES, NO What is Data Mining? Data Mining (Knowledge discovery in database) Data Mining: "The non trivial extraction of implicit, previously unknown, and potentially useful information from data" William J Frawley,

More information

Big Data Analytics. Benchmarking SAS, R, and Mahout. Allison J. Ames, Ralph Abbey, Wayne Thompson. SAS Institute Inc., Cary, NC

Big Data Analytics. Benchmarking SAS, R, and Mahout. Allison J. Ames, Ralph Abbey, Wayne Thompson. SAS Institute Inc., Cary, NC Technical Paper (Last Revised On: May 6, 2013) Big Data Analytics Benchmarking SAS, R, and Mahout Allison J. Ames, Ralph Abbey, Wayne Thompson SAS Institute Inc., Cary, NC Accurate and Simple Analysis

More information

Assessing Data Mining: The State of the Practice

Assessing Data Mining: The State of the Practice Assessing Data Mining: The State of the Practice 2003 Herbert A. Edelstein Two Crows Corporation 10500 Falls Road Potomac, Maryland 20854 www.twocrows.com (301) 983-3555 Objectives Separate myth from reality

More information

A Content based Spam Filtering Using Optical Back Propagation Technique

A Content based Spam Filtering Using Optical Back Propagation Technique A Content based Spam Filtering Using Optical Back Propagation Technique Sarab M. Hameed 1, Noor Alhuda J. Mohammed 2 Department of Computer Science, College of Science, University of Baghdad - Iraq ABSTRACT

More information

Course Syllabus. Purposes of Course:

Course Syllabus. Purposes of Course: Course Syllabus Eco 5385.701 Predictive Analytics for Economists Summer 2014 TTh 6:00 8:50 pm and Sat. 12:00 2:50 pm First Day of Class: Tuesday, June 3 Last Day of Class: Tuesday, July 1 251 Maguire Building

More information

Data Mining with SQL Server Data Tools

Data Mining with SQL Server Data Tools Data Mining with SQL Server Data Tools Data mining tasks include classification (directed/supervised) models as well as (undirected/unsupervised) models of association analysis and clustering. 1 Data Mining

More information

Data Mining for Business Intelligence. Concepts, Techniques, and Applications in Microsoft Office Excel with XLMiner. 2nd Edition

Data Mining for Business Intelligence. Concepts, Techniques, and Applications in Microsoft Office Excel with XLMiner. 2nd Edition Brochure More information from http://www.researchandmarkets.com/reports/2170926/ Data Mining for Business Intelligence. Concepts, Techniques, and Applications in Microsoft Office Excel with XLMiner. 2nd

More information

Introduction to Data Mining and Machine Learning Techniques. Iza Moise, Evangelos Pournaras, Dirk Helbing

Introduction to Data Mining and Machine Learning Techniques. Iza Moise, Evangelos Pournaras, Dirk Helbing Introduction to Data Mining and Machine Learning Techniques Iza Moise, Evangelos Pournaras, Dirk Helbing Iza Moise, Evangelos Pournaras, Dirk Helbing 1 Overview Main principles of data mining Definition

More information

Discovering, Not Finding. Practical Data Mining for Practitioners: Level II. Advanced Data Mining for Researchers : Level III

Discovering, Not Finding. Practical Data Mining for Practitioners: Level II. Advanced Data Mining for Researchers : Level III www.cognitro.com/training Predicitve DATA EMPOWERING DECISIONS Data Mining & Predicitve Training (DMPA) is a set of multi-level intensive courses and workshops developed by Cognitro team. it is designed

More information

Data Mining and Visualization

Data Mining and Visualization Data Mining and Visualization Jeremy Walton NAG Ltd, Oxford Overview Data mining components Functionality Example application Quality control Visualization Use of 3D Example application Market research

More information

The Data Mining Process

The Data Mining Process Sequence for Determining Necessary Data. Wrong: Catalog everything you have, and decide what data is important. Right: Work backward from the solution, define the problem explicitly, and map out the data

More information

HT2015: SC4 Statistical Data Mining and Machine Learning

HT2015: SC4 Statistical Data Mining and Machine Learning HT2015: SC4 Statistical Data Mining and Machine Learning Dino Sejdinovic Department of Statistics Oxford http://www.stats.ox.ac.uk/~sejdinov/sdmml.html Bayesian Nonparametrics Parametric vs Nonparametric

More information

Introduction to Data Mining

Introduction to Data Mining Introduction to Data Mining Jay Urbain Credits: Nazli Goharian & David Grossman @ IIT Outline Introduction Data Pre-processing Data Mining Algorithms Naïve Bayes Decision Tree Neural Network Association

More information

Music Classification by Composer

Music Classification by Composer Music Classification by Composer Janice Lan janlan@stanford.edu CS 229, Andrew Ng December 14, 2012 Armon Saied armons@stanford.edu Abstract Music classification by a computer has been an interesting subject

More information

Prerequisites. Course Outline

Prerequisites. Course Outline MS-55040: Data Mining, Predictive Analytics with Microsoft Analysis Services and Excel PowerPivot Description This three-day instructor-led course will introduce the students to the concepts of data mining,

More information

Anomaly and Fraud Detection with Oracle Data Mining 11g Release 2

Anomaly and Fraud Detection with Oracle Data Mining 11g Release 2 Oracle 11g DB Data Warehousing ETL OLAP Statistics Anomaly and Fraud Detection with Oracle Data Mining 11g Release 2 Data Mining Charlie Berger Sr. Director Product Management, Data

More information

Customer and Business Analytic

Customer and Business Analytic Customer and Business Analytic Applied Data Mining for Business Decision Making Using R Daniel S. Putler Robert E. Krider CRC Press Taylor &. Francis Group Boca Raton London New York CRC Press is an imprint

More information

DATA EXPERTS MINE ANALYZE VISUALIZE. We accelerate research and transform data to help you create actionable insights

DATA EXPERTS MINE ANALYZE VISUALIZE. We accelerate research and transform data to help you create actionable insights DATA EXPERTS We accelerate research and transform data to help you create actionable insights WE MINE WE ANALYZE WE VISUALIZE Domains Data Mining Mining longitudinal and linked datasets from web and other

More information

A Basic Guide to Modeling Techniques for All Direct Marketing Challenges

A Basic Guide to Modeling Techniques for All Direct Marketing Challenges A Basic Guide to Modeling Techniques for All Direct Marketing Challenges Allison Cornia Database Marketing Manager Microsoft Corporation C. Olivia Rud Executive Vice President Data Square, LLC Overview

More information

A STUDY ON DATA MINING INVESTIGATING ITS METHODS, APPROACHES AND APPLICATIONS

A STUDY ON DATA MINING INVESTIGATING ITS METHODS, APPROACHES AND APPLICATIONS A STUDY ON DATA MINING INVESTIGATING ITS METHODS, APPROACHES AND APPLICATIONS Mrs. Jyoti Nawade 1, Dr. Balaji D 2, Mr. Pravin Nawade 3 1 Lecturer, JSPM S Bhivrabai Sawant Polytechnic, Pune (India) 2 Assistant

More information

Data Science in Action

Data Science in Action + Data Science in Action Peerapon Vateekul, Ph.D. Department of Computer Engineering, Faculty of Engineering, Chulalongkorn University + Outlines 2 Data Science & Data Scientist Data Mining Analytics with

More information

Starting Smart with Oracle Advanced Analytics

Starting Smart with Oracle Advanced Analytics Starting Smart with Oracle Advanced Analytics Great Lakes Oracle Conference Tim Vlamis Thursday, May 19, 2016 Vlamis Software Solutions Vlamis Software founded in 1992 in Kansas City, Missouri Developed

More information

Introduction to Machine Learning. Speaker: Harry Chao Advisor: J.J. Ding Date: 1/27/2011

Introduction to Machine Learning. Speaker: Harry Chao Advisor: J.J. Ding Date: 1/27/2011 Introduction to Machine Learning Speaker: Harry Chao Advisor: J.J. Ding Date: 1/27/2011 1 Outline 1. What is machine learning? 2. The basic of machine learning 3. Principles and effects of machine learning

More information

Exadata V2 + Oracle Data Mining 11g Release 2 Importing 3 rd Party (SAS) dm models

Exadata V2 + Oracle Data Mining 11g Release 2 Importing 3 rd Party (SAS) dm models Exadata V2 + Oracle Data Mining 11g Release 2 Importing 3 rd Party (SAS) dm models Charlie Berger Sr. Director Product Management, Data Mining Technologies Oracle Corporation charlie.berger@oracle.com

More information

Data Analytics and Business Intelligence (8696/8697)

Data Analytics and Business Intelligence (8696/8697) http: // togaware. com Copyright 2014, Graham.Williams@togaware.com 1/40 Data Analytics and Business Intelligence (8696/8697) Introducing Data Science with R and Rattle Graham.Williams@togaware.com Chief

More information

What s Cooking in KNIME

What s Cooking in KNIME What s Cooking in KNIME Thomas Gabriel Copyright 2015 KNIME.com AG Agenda Querying NoSQL Databases Database Improvements & Big Data Copyright 2015 KNIME.com AG 2 Querying NoSQL Databases MongoDB & CouchDB

More information

TDWI Best Practice BI & DW Predictive Analytics & Data Mining

TDWI Best Practice BI & DW Predictive Analytics & Data Mining TDWI Best Practice BI & DW Predictive Analytics & Data Mining Course Length : 9am to 5pm, 2 consecutive days 2012 Dates : Sydney: July 30 & 31 Melbourne: August 2 & 3 Canberra: August 6 & 7 Venue & Cost

More information

Automated Predictive Analysis. Tomer Steinberg

Automated Predictive Analysis. Tomer Steinberg Automated Predictive Analysis Tomer Steinberg Analytics solutions from SAP SAP Analytics Portfolio Cloud Mobile Agile Visualization Advanced Analytics Big Data Enterprise Business Intelligence Collaboration

More information

Lecture/Recitation Topic SMA 5303 L1 Sampling and statistical distributions

Lecture/Recitation Topic SMA 5303 L1 Sampling and statistical distributions SMA 50: Statistical Learning and Data Mining in Bioinformatics (also listed as 5.077: Statistical Learning and Data Mining ()) Spring Term (Feb May 200) Faculty: Professor Roy Welsch Wed 0 Feb 7:00-8:0

More information

Lavastorm Analytic Library Predictive and Statistical Analytics Node Pack FAQs

Lavastorm Analytic Library Predictive and Statistical Analytics Node Pack FAQs 1.1 Introduction Lavastorm Analytic Library Predictive and Statistical Analytics Node Pack FAQs For brevity, the Lavastorm Analytics Library (LAL) Predictive and Statistical Analytics Node Pack will be

More information

An In-Depth Look at In-Memory Predictive Analytics for Developers

An In-Depth Look at In-Memory Predictive Analytics for Developers September 9 11, 2013 Anaheim, California An In-Depth Look at In-Memory Predictive Analytics for Developers Philip Mugglestone SAP Learning Points Understand the SAP HANA Predictive Analysis library (PAL)

More information

Improve Results with High- Performance Data Mining

Improve Results with High- Performance Data Mining Clementine 10.0 Specifications Improve Results with High- Performance Data Mining Data mining provides organizations with a clearer view of current conditions and deeper insight into future events. With

More information

Predictive Analytics: Too Important to Ignore The six secrets to success with predictive analytics

Predictive Analytics: Too Important to Ignore The six secrets to success with predictive analytics Predictive Analytics: Too Important to Ignore The six secrets to success with predictive analytics Webinar December 18, 2013 Sponsored by: Tony Cosentino VP & Research Director, Business Analytics Ventana

More information

KnowledgeSEEKER POWERFUL SEGMENTATION, STRATEGY DESIGN AND VISUALIZATION SOFTWARE

KnowledgeSEEKER POWERFUL SEGMENTATION, STRATEGY DESIGN AND VISUALIZATION SOFTWARE POWERFUL SEGMENTATION, STRATEGY DESIGN AND VISUALIZATION SOFTWARE Most Effective Modeling Application Designed to Address Business Challenges Applying a predictive strategy to reach a desired business

More information

TRANSACTIONAL DATA MINING AT LLOYDS BANKING GROUP

TRANSACTIONAL DATA MINING AT LLOYDS BANKING GROUP TRANSACTIONAL DATA MINING AT LLOYDS BANKING GROUP Csaba Főző csaba.fozo@lloydsbanking.com 15 October 2015 CONTENTS Introduction 04 Random Forest Methodology 06 Transactional Data Mining Project 17 Conclusions

More information

E-commerce Transaction Anomaly Classification

E-commerce Transaction Anomaly Classification E-commerce Transaction Anomaly Classification Minyong Lee minyong@stanford.edu Seunghee Ham sham12@stanford.edu Qiyi Jiang qjiang@stanford.edu I. INTRODUCTION Due to the increasing popularity of e-commerce

More information

Practical Data Science with R

Practical Data Science with R Practical Data Science with R Instructor Matthew Renze Twitter: @matthewrenze Email: matthew@matthewrenze.com Web: http://www.matthewrenze.com Course Description Data science is the practice of transforming

More information

Service courses for graduate students in degree programs other than the MS or PhD programs in Biostatistics.

Service courses for graduate students in degree programs other than the MS or PhD programs in Biostatistics. Course Catalog In order to be assured that all prerequisites are met, students must acquire a permission number from the education coordinator prior to enrolling in any Biostatistics course. Courses are

More information

Achieve Better Insight and Prediction with Data Mining

Achieve Better Insight and Prediction with Data Mining Clementine 11.1 Specifications Achieve Better Insight and Prediction with Data Mining Data mining provides organizations with a clearer view of current conditions and deeper insight into future events.

More information

Data Mining. SPSS Clementine 12.0. 1. Clementine Overview. Spring 2010 Instructor: Dr. Masoud Yaghini. Clementine

Data Mining. SPSS Clementine 12.0. 1. Clementine Overview. Spring 2010 Instructor: Dr. Masoud Yaghini. Clementine Data Mining SPSS 12.0 1. Overview Spring 2010 Instructor: Dr. Masoud Yaghini Introduction Types of Models Interface Projects References Outline Introduction Introduction Three of the common data mining

More information

A Neural Network based Approach for Predicting Customer Churn in Cellular Network Services

A Neural Network based Approach for Predicting Customer Churn in Cellular Network Services A Neural Network based Approach for Predicting Customer Churn in Cellular Network Services Anuj Sharma Information Systems Area Indian Institute of Management, Indore, India Dr. Prabin Kumar Panigrahi

More information