Data mining - all the necessary information next

Last time, different firms in its activities are actively implementing a variety of tools for digital processing of the database, thus striving to raise the level of efficiency and profitability. As a result, in the form of by-product appeared huge volumes of raw data. And many believe that these data have a great potential as a useful information.

Data mining ( sift information, data mining, data mining) is the process of identifying hidden patterns and detection in RAW data (raw data), practically useful, non-trivial, previously unknown knowledge that is easy to interpret and in all areas of life.

technology Such as Data Mining gives the possibility among the vast amounts of data to reveal patterns that cannot be detected with conventional methods of processing information. Data Mining methods are based on such scientific disciplines as: theory of databases, statistics, artificial intelligence, visualization, algorithmization etc. Use this technology in different industries, for example, soft Data Mining Ongame. By the way, to explore this technology you can write on .

the Differences of Data Mining in data processing

Conventional methods of statistical analysis or OLAP databases aimed to test pre-set hypotheses and tasks. By definition, Data Mining is needed in order to identify non-trivial patterns. The principle difference of this technology is the ability to detect such patterns, and forming hypotheses. Thus, methods of intellectual data processing is able to cope with more complex task: the formulation of a hypothesis.

Main Data Mining tasks:

• the Association
• Visualization
• Classification
• Clustering
• Sequence
• Forecasting
• Regression.

Thus, data analyst uses RAW data to find meaningful, important information, sifting through data. But the data scientist, solves a wide tasks that affect different areas of science but at the same time give excellent results.

Translated by "Yandex.Translate": translate.yandex.ru.