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Intelligence Data Mining
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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 decisionmaking.
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/nonparametric, regression algorithms, Bayesian networks, Gaussian mixture models, K-means and
hierarchical clustering, Markov models and so on.
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