...capabilities are very comprehensive. Salford systems: provides a host of predictive analytics and data mining tools for businesses. The company specialises in classification and regression tree algorithms. Its MARS algorithm was originally developed by world-renowned Stanford statistician and physicist, Jerome Friedman. The software is easy to use and learn. KXEN: is one of the few companies that is driving automated analytics. Their products, largely based on algorithms developed by the Russian mathematician Vladimir Vapnik, are easy to use, fast and can work with large amounts of data. Some users may not like the fact that KXEN works like a ‘black box’ and in most cases, it is difficult to understand and explain the results. Angoss: Like Salford systems, Angoss has developed its products around classification and regression decision tree algorithms. The advantage of...
Words: 528 - Pages: 3
...An Introduction to Data Mining Kurt Thearling, Ph.D. www.thearling.com 1 Outline — Overview of data mining — What is data mining? — Predictive models and data scoring — Real-world issues — Gentle discussion of the core algorithms and processes — Commercial data mining software applications — Who are the players? — Review the leading data mining applications — Presentation & Understanding — Data visualization: More than eye candy — Build trust in analytic results 2 1 Resources — Good overview book: — Data Mining Techniques by Michael Berry and Gordon Linoff — Web: — My web site (recommended books, useful links, white papers, …) > http://www.thearling.com — Knowledge Discovery Nuggets > http://www.kdnuggets.com — DataMine Mailing List — majordomo@quality.org — send message “subscribe datamine-l” 3 A Problem... — You are a marketing manager for a brokerage company — Problem: Churn is too high > Turnover (after six month introductory period ends) is 40% — Customers receive incentives (average cost: $160) when account is opened — Giving new incentives to everyone who might leave is very expensive (as well as wasteful) — Bringing back a customer after they leave is both difficult and costly 4 2 … A Solution — One month before the end of the introductory period is over, predict which customers will leave — If you want to keep a customer that is predicted to churn, offer them something based on their predicted...
Words: 3180 - Pages: 13
...Data Mining By Jamia Yant June 1st, 2012 Predictive Analytics and Customer Behavior “Predictive analysis is the decision science that removes guesswork out of the decision-making process and applies proven scientific guidelines to find right solution in the shortest time possible.” (Kaith, 2011) There are seven steps to Predictive Analytics: spot the business problem, explore various data sources, extract patterns from data, build a sample model using data and problem, Clarify data – find valuable factors – generate new variables, construct a predictive model using sampling and validate and deploy the model. By using this method, businesses can make fast decisions using vast amounts of data. There are three main benefits of predictive analytics: minimizing risk, indentifying fraud, and pursuing new sources of revenue. Being able to predict the risks involved with loan and credit origination, fraudulent insurance claims, and making predictions with regard to promotional offers and coupons are all examples of these benefits. It basically reduces the cost of making mistakes. This type of algorithm allows businesses to test all sorts of situations and scenarios it could take years to test in the real world. Studying customer behavior gives businesses a competitive advantage and allows them to stay ahead of the competition in their market place. Associations Discovery and Customer Purchases Association analysis is useful for discovering interesting relationships...
Words: 1650 - Pages: 7
...A * A&W (Canada) * Abilis Solutions (software development, consulting) * AbitibiBowater * Ache Records * Addition Elle (women's clothing) * Advance Gold (mining) * Access Communications * Advanced Cyclotron Systems (medical cyclotrons) * Affinity Credit Union (banking) * Areva Resources Canada (uranium) * Air Canada (airline) * AldrichPears Associates * ALDO Group * Algonquin Power * Allied Shipbuilders * Alta Newspaper Group * Alterra Power * Angoss Software Corporation (software) * Appnovation * Arc'teryx (outdoor apparel/equipment) * Atimi Software Inc * Aritzia (clothing) * Army & Navy Stores (Canada) * Arsenal Pulp Press (publisher) * ATI technologies (semiconductors) * Atmosphere Visual Effects (movie special effects) * AVI Sound International (audio/visual equipment manufacture) B * Ballard Power Systems * Banff Lodging Co * Bank of Montreal * Bank West * Barrick Gold * Bard Ventures Company * BBC Kids (television) * BC Biomedical Laboratories Ltd. * BC Hydro * BC Research Inc * Becancour Silicon (silicon manufacture) * Bell Canada * Bennett Environmental * Becker's * Ben Moss Jewellers * Big Blue Bubble (software firm) * BigPark (software firm) * Biovail * BioWare (video games) * Bison Transport Inc. (Transportation) * Black Diamond Cheese Limited * Black Hen Music (record...
Words: 1925 - Pages: 8
...1. Define data mining. Why are there many different names and definitions for data mining? Data mining is the process through which previously unknown patterns in data were discovered. Another definition would be “a process that uses statistical, mathematical, artificial intelligence, and machine learning techniques to extract and identify useful information and subsequent knowledge from large databases.” This includes most types of automated data analysis. A third definition: Data mining is the process of finding mathematical patterns from (usually) large sets of data; these can be rules, affinities, correlations, trends, or prediction models. Data mining has many definitions because it’s been stretched beyond those limits by some software vendors to include most forms of data analysis in order to increase sales using the popularity of data mining. What recent factors have increased the popularity of data mining? Following are some of most pronounced reasons: * More intense competition at the global scale driven by customers’ ever-changing needs and wants in an increasingly saturated marketplace. * General recognition of the untapped value hidden in large data sources. * Consolidation and integration of database records, which enables a single view of customers, vendors, transactions, etc. * Consolidation of databases and other data repositories into a single location in the form of a data warehouse. * The exponential increase...
Words: 4581 - Pages: 19
...Fundamentals of Database Systems Preface....................................................................................................................................................12 Contents of This Edition.....................................................................................................................13 Guidelines for Using This Book.........................................................................................................14 Acknowledgments ..............................................................................................................................15 Contents of This Edition.........................................................................................................................17 Guidelines for Using This Book.............................................................................................................19 Acknowledgments ..................................................................................................................................21 About the Authors ..................................................................................................................................22 Part 1: Basic Concepts............................................................................................................................23 Chapter 1: Databases and Database Users..........................................................................................23 ...
Words: 229471 - Pages: 918
...CONTENTS: CASE STUDIES CASE STUDY 1 Midsouth Chamber of Commerce (A): The Role of the Operating Manager in Information Systems CASE STUDY I-1 IMT Custom Machine Company, Inc.: Selection of an Information Technology Platform CASE STUDY I-2 VoIP2.biz, Inc.: Deciding on the Next Steps for a VoIP Supplier CASE STUDY I-3 The VoIP Adoption at Butler University CASE STUDY I-4 Supporting Mobile Health Clinics: The Children’s Health Fund of New York City CASE STUDY I-5 Data Governance at InsuraCorp CASE STUDY I-6 H.H. Gregg’s Appliances, Inc.: Deciding on a New Information Technology Platform CASE STUDY I-7 Midsouth Chamber of Commerce (B): Cleaning Up an Information Systems Debacle CASE STUDY II-1 Vendor-Managed Inventory at NIBCO CASE STUDY II-2 Real-Time Business Intelligence at Continental Airlines CASE STUDY II-3 Norfolk Southern Railway: The Business Intelligence Journey CASE STUDY II-4 Mining Data to Increase State Tax Revenues in California CASE STUDY II-5 The Cliptomania™ Web Store: An E-Tailing Start-up Survival Story CASE STUDY II-6 Rock Island Chocolate Company, Inc.: Building a Social Networking Strategy CASE STUDY III-1 Managing a Systems Development Project at Consumer and Industrial Products, Inc. CASE STUDY III-2 A Make-or-Buy Decision at Baxter Manufacturing Company CASE STUDY III-3 ERP Purchase Decision at Benton Manufacturing Company, Inc. CASE STUDY III-4 ...
Words: 239887 - Pages: 960
