Business Intelligence Data Mining

Data mining can be technically defined as the automated extraction of hidden information from large databases for predictive analysis. In other words, it is the retrieval of useful information from large masses of data, which is also presented in an analyzed form for specific decision-making.

Data mining requires the use of mathematical algorithms and statistical techniques integrated with software tools. The final product is an easy-to-use software package that can be used even by non-mathematicians to effectively analyze the data they have. Data Mining is used in several applications like market research, consumer behavior, direct marketing, bioinformatics, genetics, text analysis, fraud detection, web site personalization, e-commerce, healthcare, customer relationship management, financial services and telecommunications.

Business intelligence data mining is used in market research, industry research, and for competitor analysis. It has applications in major industries like direct marketing, e-commerce, customer relationship management, healthcare, the oil and gas industry, scientific tests, genetics, telecommunications, financial services and utilities. BI uses various technologies like data mining, scorecarding, data warehouses, text mining, decision support systems, executive information systems, management information systems and geographic information systems for analyzing useful information for business decision making.

Business intelligence is a broader arena of decision-making that uses data mining as one of the tools. In fact, the use of data mining in BI makes the data more relevant in application. There are several kinds of data mining: text mining, web mining, social networks data mining, relational databases, pictorial data mining, audio data mining and video data mining, that are all used in business intelligence applications.

Some data mining tools used in BI are: decision trees, information gain, probability, probability density functions, Gaussians, maximum likelihood estimation, Gaussian Baves classification, cross-validation, neural networks, instance-based learning /case-based/ memory-based/non-parametric, regression algorithms, Bayesian networks, Gaussian mixture models, K-means and hierarchical clustering, Markov models and so on.