Data Mining.

•Data mining has been defined as "the nontrivial extraction of implicit, previously unknown, and potentially useful information from data" and "the science of extracting useful information from large data sets or databases"

•Data mining, the extraction of hidden predictive information from large databases, is a powerful new technology with great potential to help companies focus on the most important information in their data warehouses.

•Data mining tools predict future trends and behaviors, allowing businesses to make proactive, knowledge-driven decisions.
• Data mining tools can answer business questions that were traditionally too time consuming.
• it is a natural evolution of query and reporting tools.
• everyone who creates queries and reports thn thy get benifits from data mining.

•Modern data mining systems self learn from the previous history of the investigated system, formulating and testing hypotheses about the rules, which the system obeys.

••••Evolution of Data Mining••••
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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:
1.Massive data collection
2.Powerful multiprocessor computers
3.Data mining algorithms

•The evolution of data mining has the following phases:
4.Data collection (1960s)
5.Data access (1980s)
6.Data warehousing & decision support (1990s)
7.Data mining (1990s onwards)

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•••Advantages of Data Mining••••

•Data mining techniques can yield the benefits of automation on existing software and hardware platforms, and can be implemented on new systems, as existing platforms are upgraded and new products developed.

•When data mining tools are implemented on high performance parallel processing systems, they can analyze massive databases in minutes. The high speed makes it practical for users to analyze huge quantities of data.

•Given databases of sufficient size and quality, data mining technology can generate new business opportunities by providing the following capabilities:

1.Automated prediction of trends and behaviors
—Data mining automates the process of finding predictive information in large databases.

2.Automated discovery of previously unknown patterns
—Data mining tools sweep through databases and identify previously hidden patterns in one step.

3.Databases can be larger in both depth and breadth
—High performance data mining allows users to explore the full depth of a database, without pre-selecting a sub-set of variables. The data mining databases contains larger samples (more rows) as they yield lower estimation errors and variance, and allow users to make inferences about small but important segments of a population.
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++ Techniques used in data mining.

1). Neural networks.
- Non-linear predictive models that learn through training and resemble biological neural networks in structure.

2). Rule indication.
- The extraction of useful if-then rules from data based on statistical significance.

3). Evolutionary programming ;
- Today it's the youngest and evidently the most promising branch of data mining.
- The underlying idea of the method is that the system automatically formulates hypotheses about