Today >>

The Fundamentals of Data Mining

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

• Massive data collection.

• Powerful multiprocessor computers.

• Data Mining Algorithms.

The commercial databases are growing at an unprecedented rate. A recent study by Meta Group on Data Warehouse projects found that 19% of respondents are above the level of 50 Gigabytes, while 59% expect to achieve in the second quarter 1997. In some industries such as retail (retail), these numbers may be even greater. MCI Telecommunications Corp. has a database of 3 terabytes + 1 terabyte indexing and running overhead MVS on IBM SP2. The need for parallel computational engines improvements can now be achieved more cost - effective with technonology parallel multiprocessor computers. Algorithms Data Mining techniques have been used at least for 10 years but only recently have been implemented as tools mature, reliable, understandable that consistently are more performant that classical statistical methods.

In the evolution from business data to business information, gives new step builds on the previous one. For example, access to dynamic data applications is critical for navigation data (drill-through applications), and the ability to store large databases is critical to Data Mining. The essential components of data mining technology have been under development for decades in research areas such as statistics, intelligent gency artificial and machine learning. Today, the maturity of these techniques, engines with relational databases, high performance,statements these technologies were not practicable for data warehouse environments today.