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Data Mining: Software Helping Business Run
Group 4 Austin Beam, Brittany Dearien, Warren Irwin, Amanda Medlin, Rob Westerman
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Introduction Data Mining defined Basic Facts Goals of data mining
Steps to data mining What data mining can do Data mining in business Advantages/Disadvantages Data Mining Software Data Mining in the future
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Data Mining Data mining is defined as
The science of extracting useful information from large data sets or databases Also known as Knowledge-Discovery in Databases (KDD)
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Basic Facts Data mining presents information that would not be available otherwise The more data the better! Must have good data or the solutions are irrelevant
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Automated Hidden Predictive
Goals of Data Mining Simplification and automation of the overall statistical process from data sources to model application This means: The automated extraction of hidden predictive information from large databases Automated Hidden Predictive
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Steps to Data Mining Data mining relieves the pressure and need for as many statisticians Begin with a Predictive Model: take various information such as family, age, income to answer a question Mathematical Algorithms
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How the data is scored Qualitative view: Quantitative view:
The way data is received/recorded Qualitative view: provides insight into the data you are working with, but requires interaction capabilities and good visualization Quantitative view: more of an automated process and a bottom line orientation
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Decision Trees A series of ‘if/then’ questions that reach a final solution
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Convergence of 3 Technologies
Increased Computing Power Improved Data Collection and Management Statistical Algorithms DM
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What can Data Mining do? Other than help find new information, data mining can assist in Finding new patterns Recognizing significant facts Valuing customer loyalty Following new and changing trends
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Data Mining in Business
Market segmentation Customer churn Fraud detection Direct marketing Interactive marketing Market basket analysis Trend analysis
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Advantages of Data Mining
Automated predictions of trends and behaviors Discovery of previously unknown patterns More time/cost efficient than statisticians Competitive advantage Increased Profitability
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Disadvantages of Data Mining
Is the data correct? Who has the right to this information? Privacy Ethics
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Data Mining Software SAS SPSS Insightful (formerly Mathsoft/S-Plus)
800 Pound Gorilla in the data analysis space SPSS Insightful (formerly Mathsoft/S-Plus) Well respected statistical tools, now moving into mining Oracle Integrated data mining into the database Angoss One of the first data mining applications (as opposed to tools) HNC Very specific analytic solutions Unica Great mining technology, focusing less on analytics these days
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Data Mining in the Future
Growing Trends Data Mining market size of software has grown from $540M in 2002, $1.5B in 2005 Endless possibilities for everyday life!
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