Business Intelligence Data Mining

Data mining can be technically defined as thetechnologies like data mining, scorecarding, data
automated extraction of hidden information from largewarehouses, text mining, decision support systems,
databases for predictive analysis. In other words, it isexecutive information systems, management
the retrieval of useful information from large massesinformation systems and geographic information
of data, which is also presented in an analyzed formsystems for analyzing useful information for business
for specific decision-making.Data mining requires thedecision making.Business intelligence is a broader arena
use of mathematical algorithms and statisticalof decision-making that uses data mining as one of the
techniques integrated with software tools. The finaltools. In fact, the use of data mining in BI makes the
product is an easy-to-use software package that candata more relevant in application. There are several
be used even by non-mathematicians to effectivelykinds of data mining: text mining, web mining, social
analyze the data they have. Data Mining is used innetworks data mining, relational databases, pictorial
several applications like market research, consumerdata mining, audio data mining and video data mining,
behavior, direct marketing, bioinformatics, genetics, textthat are all used in business intelligence
analysis, fraud detection, web site personalization,applications.Some data mining tools used in BI are:
e-commerce, healthcare, customer relationshipdecision trees, information gain, probability, probability
management, financial services anddensity functions, Gaussians, maximum likelihood
telecommunications.Business intelligence data mining isestimation, Gaussian Baves classification,
used in market research, industry research, and forcross-validation, neural networks, instance-based
competitor analysis. It has applications in majorlearning /case-based/ memory-based/non-parametric,
industries like direct marketing, e-commerce, customerregression algorithms, Bayesian networks, Gaussian
relationship management, healthcare, the oil and gasmixture models, K-means and hierarchical clustering,
industry, scientific tests, genetics, telecommunications,Markov models and so on.
financial services and utilities. BI uses various