1. V.Loganayagi@Abinaya.
K.P.Unnamalai.
B.sc. computer science
Iii year(I Shift)

2.  Data mining an interdisciplinary subfield of
computer science is the computational process of
discovering patterns in large datasets.
 Involving methods at the intersection of artificial
intelligence, machine learning, statistics, and
database systems.
 Data mining process is to extract information from
a data set and transform it into an understandable
structure for further use.

4.  There are a number of applications that data mining
has.
 Banking: Loan/credit card approval
 Predict good customers based on old customers
 Customer relationship management:
 Identify those who are likely to leave for a competitor.
 Targeted marketing:
 Identify likely responders to promotions
 Fraud detection: Telecommunications, financial
transactions
 From an online stream of event identify fraudulent
events

5.  Manufacturing and production:
 Automatically adjust knobs when process
parameter changes
 Medicine: disease outcome, effectiveness of
treatments
 Analyze patient disease history: find relationship
between diseases
 Molecular/Pharmaceutical: Identify new drugs
 Scientific data analysis:
 Identify new galaxies by searching for sub clusters
 Web site/store design and promotion:
 Find affinity of visitor to pages and modify layout

6.  The term Knowledge Discovery in Databases, or
KDD for short, refers to the broad process of finding
knowledge in data, and emphasizes the "high-level"
application of particular data mining methods.
 It is of interest to researchers in machine
learning, pattern
recognition, databases, statistics, artificial
intelligence, knowledge acquisition for expert
systems, and data visualization.
 The unifying goal of the KDD process is to extract
knowledge from data in the context of large
databases.

9.  Several core techniques that are used in data
mining describe the type of mining and data
recovery operation.
 Unfortunately, the different companies and
solutions do not always share terms, which can add
to the confusion and apparent complexity.
 Let's look at some key techniques and examples of
how to use different tools to build the data mining.

13. Clustering

14.  Data mining itself relies upon building a suitable data model and
structure that can be used to process, identify, and build the
information that you need.
 Regardless of the source data form and structure, structure and
organize the information in a format that allows the data mining
to take place in as efficient a model as possible.
 Depending on your data source, how you build and translate this
information is an important step, regardless of the technique you
use to finally analyze the data

16. Marking/Retailing:
 Data mining can aid direct marketers by providing them with
useful and accurate trends about their customers’ purchasing
behavior.
 Based on these trends, marketers can direct their marketing
attentions to their customers with more precision.
 For example, marketers of a software company may
advertise about their new software to consumers who have a
lot of software purchasing history.
 In addition, data mining may also help marketers in
predicting which products their customers may be interested
in buying.

17. Banking/Crediting:
Data mining can assist financial institutions in areas
such as credit reporting and loan information.
 For example, by examining previous customers with
similar attributes, a bank can estimated the level of risk
associated with each given loan.
 In addition, data mining can also assist credit card
issuers in detecting potentially fraudulent credit card
transaction.
 Although the data mining technique is not a 100%
accurate in its prediction about fraudulent charges, it
does help the credit card issuers reduce their losses.

18. Law enforcement:
 Data mining can aid law enforcers in identifying
criminal suspects as well as apprehending these
criminals by examining trends in location, crime
type, habit, and other patterns of behaviors.
Researchers:
 Data mining can assist researchers by speeding up
their data analyzing process; thus, allowing them
more time to work on other projects.

19. Privacy Issues:
 Personal privacy has always been a major concern in
this country. In recent years, with the widespread use
of Internet, the concerns about privacy have increase
tremendously. Because of the privacy issues, some
people do not shop on Internet.
 Although it is against the law to sell or trade personal
information between different organizations, selling
personal information have occurred.
 The selling of personal information may also bring
harm to these customers because you do not know
what the other companies are planning to do with the
personal information that they have purchased.

20. Security issues:
 Although companies have a lot of personal
information about us available online, they do not
have sufficient security systems in place to protect
that information.
 This incidence illustrated that companies are
willing to disclose and share your personal
information, but they are not taking care of the
information properly.
 With so much personal information
available, identity theft could become a real
problem.

21. Misuse of information/inaccurate information:
 Trends obtain through data mining intended to be
used for marketing purpose or for some other ethical
purposes, may be misused.
 Unethical businesses or people may used the
information obtained through data mining to take
advantage of vulnerable people or discriminated
against a certain group of people.
 In addition, data mining technique is not a 100
percent accurate; thus mistakes do happen which can
have serious consequence.

22. “Delivering
results that
endure just what
you needed”