Data Mining Services Used By Business Intelligence

Data mining can be technically define the automatic removal of hidden information from large databases, predictive analysis. In other words, it should get useful information from large amounts of data, which was also analyzed in the form of specific decisions.

Data mining requires the use of mathematical algorithms and statistical techniques integrated into the software. The end product is easy to use software package that can also be used by mathematicians to effectively analyze the data they have used. Data mining is used in various applications such as market research, consumer behavior, direct marketing, bioinformatics, genetics, textual analysis, fraud detection, Web site personalization, electronic commerce, healthcare, customer relationship management, financial services and telecommunications.

Business intelligence data mining is used for market research, industrial research and competitor analysis. It also has applications in key sectors such as direct marketing, e-commerce, customer relationship management, healthcare, oil and gas industry, science experiments, genetics, telecommunications, financial services and utilities. BI various techniques, such as the use of data mining, scorecarding, data warehouses, text mining, decision support systems, management information systems, management information systems and geographic information systems analysis of useful information for making business decisions.

Business intelligence is a broader arena of using data mining tools in one. In fact, the use of data mining, BI makes information more relevant in the application. There are several data mining: text mining, web mining, social networks, data mining, data mining in relational databases with images, audio and video data mining data mining, that all use business intelligence applications.

Some data mining tools are used to the BI are: decision trees, access to information, the probability, density function, Gaussian, maximum likelihood estimation, Gaussian Baves classification, cross validation, neural networks, such as learning / case-based / memory-based / nonparametric regression algorithms, Bayesian networks, Gaussian mixture models, K-means and hierarchical clustering, Markov models, and so on.

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