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Department of CSE&IT Article
Article No: 201607CSEIT08
Article Title: DATA MINING
K.S.Janani , D.Haripriya , V.Sumathi
IV- year Computer Science and Engineering
Tejaa Shakthi Institute Of Technology For Women
Coimbatore.
Introduction:
Data mining is the process of analyzing data from different perspective.
Summarizing it into the useful information . Information that can be used to increase
revenue , cut cost , or both . It involves two approaches such a
 Bottom up
 Top down
Bottom up is to explore raw facts to find connections. Top down is to search test
hypothesis.The objective of data mining is to identify valid ,novels, potentially useful
& correlations understandable & existing data in pattern. It createsboth insights &
data that add to the knowledge of the organization. Inferring new information from
already collected data. Data mining Techniques involues sophisticated algorithms
including decision tree classification ,association detection& clustering. Data mining
which is also known as Knowledge discovery .It is the process in which we extract
useful information from the large set of the data.
In an iteration way, the following steps from the data of useful knowledge to be obtain
are as follows,
Step 1: Data processing:
The data processing includes the basic operation such as
 Data selection --The revelent data is retrieve to the KDD task from the database.
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Department of CSE&IT Article
Article No: 201607CSEIT08
 Data cleaning-- The missing data fields handles due to removing noise and also
to remove inconsistent data.
 Data integration -- From multiple sources it is to combine data.
Step 2: Data transformation:
For the task of mining ,the data is appropriate to transform ie, for the
purpose to represent the data. There are the two basic operations performed such as
 Feature selection -- The useful data to be select.
 Feature transformation--. The selected data to be transformed.
Step 3: Data mining:
It is the process of intelligent methods which was extract data patterns.
Step 4: Patterns: It used to evaluate and for presentation purpose.
fig(1)-Steps of KDD process
Functionalities of Data mining:
To specifying the pattern kinds in which to be found in the task of
data mining. The tasks are of two categories such as,
(i) .Descriptive (ii).Predictive
Descriptive – The task of the data which is to represents as properties in the
database are in descriptive mining tasks.
Predictive - In order to make prediction, inference perform on current data.
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Department of CSE&IT Article
Article No: 201607CSEIT08
For different accommodate of user expectations and applications ,the data mining
systems mined the multiple kinds of patterns respectively.
In different levels of abstractions ie, granularities, a data mining systems are able to
discover patterns.
Prediction vs Classification:
Prediction is the continuous of the valued functions ie, values which was
missed and to predict the unknown data.
Classification in which the data classification are of training set and labels
in class (class labels) in attribute classifying and uses in new data.
Data mining using different databases:




Data mining
Data mining
Data mining
Data mining
projects
projects
projects
projects
using
using
using
using
JAVA
PHP
.NET
MAT LAB.
Data mining Applications:
 Banking : loan \ credit card approval
Based on old customers they can predict good customer.
 Customer relationship management.
Identify the competitor who going to leave.
 Targeted marketing:
Identify likely responders to promotion.
 Fraud detection : telecommunication,financial transaction:
The fraudulent events can be identified .
 Manufacturing and production;
The Knobs can be adjust automatically.
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Department of CSE&IT Article
Article No: 201607CSEIT08
 Medicine: disease outcomes ,effectiveness of treatments:
The patient diseases history can be analyze and find the relationship
between diseases.
fig(2). Data mining Paradigms
Advantages of Data mining:
1.
2.
3.
4.
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Improves customers satisfaction and services.
Saves time and money.
Increases sales effectiveness.
Increases profitability.
Tejaa Shakthi Institute of Technology for women,Coimbatore.
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Department of CSE&IT Article
Article No: 201607CSEIT08
Disadvantages of Data mining:
1. Privacy issues.
2. Security issues.
3. Misuse of information and inaccurate information.
Conclusion:
For the business , society , government as well as individuals the data mining
plays a lots of benefits. For enrollment management the use of data mining as new
development. Currently data mining is done on simple numeric and category wise data.
More complex data types may include in future . Research in data mining will result in
new methods to determine the most interesting characteristics in the data. As models
are developed and implemented, they can be used as a tools in enrollment
management.
References:
1. http://www-user.cs.umn.edu/~kumar/dmbook/index.php
2. http://www.zentut.com/data-mining/advantages-and-disadvantages-of-datamining/
3. http://www.lovelycoding.org/2015/08/21-best-data-mining-project-ideasstudents
4. http://www.newstack.io/six-of-the-best-open-source-data-mining-tools/
5. https://www.google.co.in/search?q=data+mining&biw=800&bih=462&source=l
nms.
Reviewed By,
Approved By,
Authenticated By,
Mr.M.S.Vijay Kumar AP/CSE
Mr.A.M.SenthilKumar HOD/CSE
Dr.N.J.R.Muniraj
PRINCIPAL, TSITW.
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