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Summary

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We live in the age of information. The importance of collecting data that reflect your business or scientific activities to achieve competitive advantage is widely recognized now. Powerful systems for collecting data and managing it in large databases are already in place in most large and mid-range companies. However, the bottleneck of turning this data into your success is the difficulty of extracting knowledge about the system that you study from the collected data. Consider the following questions:

What goods should be promoted to this customer?
What is the probability that a certain customer will respond to a planned promotion?
Can one predict the most profitable securities to buy/sell during the next trading session?
Will this customer default on a loan or pay back on schedule?
What medical diagnosis should be assigned to this patient?
How large the peak loads of a telephone or energy network are going to be?
Why the facility suddenly starts producing defective goods?

These are all the questions that can probably be answered if information hidden among megabytes of data in your database can be found explicitly and utilized. Modeling the investigated system and discovering relations that connect variables in a database is the objective of data mining.

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Brainwave These are all the questions that can probably be answered if information hidden among megabytes of data in your database can be found explicitly and utilized. Modeling the investigated system and discovering relations that connect variables in a database is the objective of data mining.

What goods should be promoted to this customer?
What is the probability that a certain customer will respond to a planned promotion?
Can one predict the most profitable securities to buy/sell during the next trading session?
Will this customer default on a loan or pay back on schedule?
What medical diagnosis should be assigned to this patient?
How large the peak loads of a telephone or energy network are going to be?
Why the facility suddenly starts producing defective goods?

Modern data mining systems self learn from the previous history of the investigated system, formulating and testing hypotheses about the rules, which this system obeys. When concise and valuable knowledge about the system of interest had been discovered, it can and should be incorporated into some decision support system, which helps the manager to make wise and informed business decisions.

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Data mining techniques are the result of a long process of research and product development. This evolution began when business data was first stored on computers, continued with improvements in data access, and more recently, generated technologies that allow users to navigate through their data in real time. Data mining takes this evolutionary process beyond retrospective data access and navigation to prospective and proactive information delivery. Data mining is ready for application in the business community because it is supported by three technologies that are now sufficiently mature: massive data collection, powerful multiprocessor computers and data mining algorithms.

Commercial databases are growing at unprecedented rates. The accompanying need for improved computational engines can now be met in a cost-effective manner with parallel multiprocessor computer technology. Data mining algorithms embody techniques that have existed for at least 10 years, but have only recently been implemented as mature, reliable, understandable tools that consistently outperform older statistical methods. In the evolution from business data to business information, each new step has built upon the previous one. For example, dynamic data access is critical for drill-through in data navigation applications, and the ability to store large databases is critical to data mining. From the user's point of view, the four steps listed below were revolutionary because they allowed new business questions to be answered accurately and quickly.