Business Intelligence Data Mining

Data mining can be technically defined as theuses various technologies like data mining,
automated extraction of hidden information fromscorecarding, data warehouses, text mining, decision
large databases for predictive analysis. In othersupport systems, executive information systems,
words, it is the retrieval of useful information frommanagement information systems and geographic
large masses of data, which is also presented in aninformation systems for analyzing useful information
analyzed form for specific decision-making.Data miningfor business decision making.Business intelligence is a
requires the use of mathematical algorithms andbroader arena of decision-making that uses data
statistical techniques integrated with software tools.mining as one of the tools. In fact, the use of data
The final product is an easy-to-use software packagemining in BI makes the data more relevant in
that can be used even by non-mathematicians toapplication. There are several kinds of data mining:
effectively analyze the data they have. Data Mining istext mining, web mining, social networks data mining,
used in several applications like market research,relational databases, pictorial data mining, audio data
consumer behavior, direct marketing, bioinformatics,mining and video data mining, that are all used in
genetics, text analysis, fraud detection, web sitebusiness intelligence applications.Some data mining
personalization, e-commerce, healthcare, customertools used in BI are: decision trees, information gain,
relationship management, financial services andprobability, probability density functions, Gaussians,
telecommunications.Business intelligence data mining ismaximum likelihood estimation, Gaussian Baves
used in market research, industry research, and forclassification, cross-validation, neural networks,
competitor analysis. It has applications in majorinstance-based learning /case-based/ memory-based
industries like direct marketing, e-commerce,non-parametric, regression algorithms, Bayesian
customer relationship management, healthcare, the oilnetworks, Gaussian mixture models, K-means and
and gas industry, scientific tests, genetics,hierarchical clustering, Markov models and so on.
telecommunications, financial services and utilities. BI