Business Intelligence Data Mining

Data mining can be determined by an automatic picking, hidden in large databases for analysis. In other words, requires the recovery of useful information about the large amounts of data, which even at a particular manner of implementation of mining making.Data to analyze the use of mathematical algorithms and statistical techniques integrated software tools. The final product is easy to use software that can be used effectively for non-mathematicians to the data that they use to analyze. Data mining has in various applications, such as market research, consumer behavior, direct marketing, genetics, bioinformatics, text analysis, fraud detection, personalization sites, e-commerce, healthcare, customer relationship management, financial services and data mining telecommunications.Business in the market, the industrial been used research and competitive analysis. It is important for applications such as direct mail, e-commerce, customer relationship management, health, oil and gas industry, the scientific evidence, genetics, telecommunications, financial services and utilities. BI uses a variety of techniques, such as data mining, performance evaluation, data storage, text mining, decision support, management information systems, management information systems and geographic information systems for the analysis of the necessary information to business decisions is the decision of a broader intelligence making.Business field can use the Data Mining Tool. In fact, the BI data mining application makes the relevance of the application data. There are different types of data mining, text mining, web mining, social networks, data mining, relational databases, data mining, graphic, audio and video data mining, data mining, which are used for business intelligence applications . Some data mining tools are used to the BI: decision trees, data, probability, probability density function, Gaussian maximum likelihood estimation, Gaussian Baves classification, cross validation, neural networks, such as learning / base case / base / storage algorithms , nonparametric regression, Bayesian networks, Gaussian mixed models, the k-means and hierarchical clustering, Markov models, etc. .