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Business Intelligence Data Mining

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




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