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Database Administration
&
Data Mining
Project on
Data Mining Concepts
Presentation By: sidra khan
Roll No: 24
Class: Ty. BBA
Subject Professor: Prof. Madhuri Dhavan
INTRODUCTION
TO
DATA MINING CONCEPTS
Data mining is the process of extracting and discovering patterns in large
data sets involving methods at the intersection of machine learning,
statistics and database system. Database mining is interdisciplinary
subfield of computer science and statistics with an overall goal of
extracting information with intelligent methods from a data set and
transforming the information into a comprehensible structure for the
further use.
A data mining and data analytic are different components of data science
and operates in interrelated manner. It can be often be treated as the
same, but they are slightly different in scale. Data analytics is a broader
with data analytics being one of its subcomponents. Data mining is mainly
used in the for the process of extracting, filtering, uncovering past
tendencies and predicting from data set. Its mostly performed by
computer scientist and data engineers.
STEPS OF MINNING IN PROCESS
The following steps are as follows:
1)Data Cleaning: Data cleaning is the process where data gets cleaned.
Data in the real world is normally incomplete noisy and inconsistent. The
data available in the data source might be lacking attribute values data of
interest. Sometimes a data may contain errors or outliers.
2) Data integration: Data integration is the process where data from
different data source are integrated into one. Data integration is the real
complex and tricky task because data from different source does not
match normally. Data integration tries to reduce redundancy to the
maximum possible level without affecting the reliability of data.
3) Data Selection: Data mining process requires a large volume of
historical data for analysis. So, usually the data repository with integrated
data contains much more data than actually required.
4)Data Transformation: Data transformation is the process of transforming
and consolidating the data into different forms that are suitable for
mining. Data transformation normally involves normalization, aggregation
and generalization.
5) Data Mining: Data mining is the core process where a number of
complex and intelligent method are applied to extract patterns from data
which includes a number of tasks such as association, classification,
prediction, clustering and time series analysis and so on.
6) Pattern Evaluation: The pattern evaluation identifies the truly
interesting patterns representing knowledgeable based on different types
of interesting measures.
7) Knowledge Representation: The information mined from the data
needs to be presented to the users in an appealing way. Different
knowledge representation and visualization techniques are applied to
provide the output of data mining to the users.
NEED OF
DATA MINING
Data Mining is the process of analysing a large batch of information to
discuss trends and patterns.
*Data mining can be used by corporation for everything from learning
about what customers are interested in or want to buy to fraud detection
and spam.
* Data mining programs break down patterns and connection in data
based on what information users request or provide.
* Social Media companies use data mining techniques to commodify their
users in order to generate profit.
* This use of data Mining ha s come under criticism lately as users are
often unaware of data mining happening with their personal information,
especially when it is used to influence preference.
* It includes the increase customer loyalty, It identifies hidden
profitability, Minimizes client’s involvement and Customer’s satisfaction.
DATA MINING
APPLICATIONS
Data mining is primarily used by organization with
Intense consumer demands- Retail, communication,
Financial, marketing company determine price.
Consumer preferences, product positioning and
Impact on sales, customer satisfaction, and
corporate profits. Data mining enables a retailer to
use point-of-sale records of customer purchases
to develop products and promotion that help
the organization to attract the customer.
ADVANTAGES OF
DATA MINING
The advantages of Data mining such as:
1)Predict Future Trends
2)Decision Making
3)Cost Reduction
4)Fast and Feasible Decision &
5)Signifies Customer Habits
*The Data mining techniques organization to obtain knowledge-based
data.
*Data mining enables organization to make lucrative modification in
operation and production.
*Compared with other statistical data application, data mining is a cost
efficient.
*Data mining helps the decision-making process of an organization.
*It Facilitates the automated discovery of hidden patterns as well as the
prediction of trends and behaviours.
*It can be induced in the new system as well as the existing platforms.
*It is a quick process that makes it easy for a new user to analyse
enormous of data in a short time.
DISADVANTAGES OF
DATA MINING
The following Disadvantages of Data Mining such as:
1)Violates user Privacy
2)Additional irrelevant information
3)Misuse of information &
4)Accuracy of data
*There is a probability that the organization may sell useful data of
customer to other organization for money. As per the report, American
Express has sold credit card purchases of their customer to other
organizations.
*Many Data Mining analytics software is difficult to operate and needs
advance training to work on.
*Different Data Mining instruments operate in the distinct ways due to
the different algorithms used in their design. Therefore, the selection of
the right data mining tools is a very challenging task.
*The Data Mining techniques are not precise, so that it may lead to severe
consequences in the certain conditions.
Thank you
SIDRA KHAN
T.Y. B.B.A
DATABASE ADMINISTRATION AND DATA MINING
