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Business Intelligence Data Mining

Data mining can be technically defined as theutilities. BI uses various technologies like
automated extraction of hidden informationdata mining, scorecarding, data warehouses,
from large databases for predictive analysis.text mining, decision support systems,
In other words, it is the retrieval of usefulexecutive information systems, management
information from large masses of data, whichinformation systems and geographic
is also presented in an analyzed form forinformation systems for analyzing useful
specific decision-making.Data mining requiresinformation for business decision
the use of mathematical algorithms andmaking.Business intelligence is a broader
statistical techniques integrated witharena of decision-making that uses data
software tools. The final product is anmining as one of the tools. In fact, the use
easy-to-use software package that can be usedof data mining in BI makes the data more
even by non-mathematicians to effectivelyrelevant in application. There are several
analyze the data they have. Data Mining iskinds of data mining: text mining, web
used in several applications like marketmining, social networks data mining,
research, consumer behavior, directrelational databases, pictorial data mining,
marketing, bioinformatics, genetics, textaudio data mining and video data mining, that
analysis, fraud detection, web siteare all used in business intelligence
personalization, e-commerce, healthcare,applications.Some data mining tools used in
customer relationship management, financialBI are: decision trees, information gain,
services and telecommunications.Businessprobability, probability density functions,
intelligence data mining is used in marketGaussians, maximum likelihood estimation,
research, industry research, and forGaussian Baves classification,
competitor analysis. It has applications incross-validation, neural networks,
major industries like direct marketing,instance-based learning /case-based/
e-commerce, customer relationship management,memory-based/non-parametric, regression
healthcare, the oil and gas industry,algorithms, Bayesian networks, Gaussian
scientific tests, genetics,mixture models, K-means and hierarchical
telecommunications, financial services andclustering, Markov models and so on.



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