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Overview of Data Mining

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InternationalJournal of Trend in Scientific Research and Development (IJTSRD)

Volume 4 Issue 4, June 2020 Available Online: e-ISSN: 2456 – 6470

@ IJTSRD | Unique Paper ID –


| Volume – 4 | Issue – 4 | May-June 2020 Page 1333

Overview of Data Mining

Rupashi Koul

Department of Computer Science Engineering, Dronacharya College of Engineering, Gurugram, Haryana, India


Data mining is the process of discovering patterns in large data sets involving

methods at the intersection of machine learning, statistics, and database

systems.[1] Data mining is an interdisciplinary sub field of computer science

and statistics with an overall goal to extract from a data set and transform the

information into a comprehensible structure for further use.[1][2][3][4] The

process of digging through data to discover hidden connections and predict

future trends has a long history. Sometimes referred to as ‘knowledge

discovery’ in databases, the term data mining wasn’t coined until the 1990s.

What was old is new again, as data mining technology keeps evolving to keep

pace with the limitless potential of big data and affordable computing power.

Over the last decade, advances in processing power and speed have enabled us

to move beyond manual, tedious and time-consuming practices to quick, easy

and automated data analysis. The more complex the data sets collected, the

more potential there is to uncover relevant insights.

KEYWORDS: database, data mining, techniques

How to cite this paper: Rupashi Koul

“Overview of Data Mining” Published in

International Journal

of Trend in Scientific

Research and


(ijtsrd), ISSN: 2456-

6470, Volume-4 |

Issue-4, June 2020,

pp.1333-1336, URL:

Copyright © 2020 by author(s) and

International Journal of Trend in Scientific

Research and Development Journal. This

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The manual extraction of patterns from data has occurred

for centuries. Early methods of identifying patterns in data

include Bayes’ theorem (1700s) and regression analysis

(1800s). The proliferation, ubiquity and increasing power of

computer technology have dramatically increased data

collection, storage, and manipulation ability. As data sets

have grown in size and complexity, direct data analysis has

increasingly been augmented with indirect, automated data

processing, aided by other discoveries in computer science,

specially in the field of machine learning, such as neural

networks, cluster analysis, genetic algorithms (1950s),

decision trees and decision rules (1960s), and support

vector machines (1990s). Data mining is the process of

applying these methods with the intention of uncovering

hidden patterns[5] in large data sets. It bridges the gap from

applied statistics and artificial intelligence to database

management by exploiting the way data is stored and

indexed in databases to execute the actual learning and

discovery algorithms more efficiently, allowing such

methods to be applied to ever-larger data sets. mining is

widely used in diverse areas. There are a number of

commercial data mining system available today and yet

there are many challenges in this field. Some of the areas in

which data mining is used is as follows:

A. Retail Industry
Data Mining has its great application in Retail Industry

because it collects large amount of data from on sales,

customer purchasing history, goods transportation,

consumption and services. It is natural that the quantity of

data collected will continue to expand rapidly because of the

increasing ease, availability and popularity of the web. Data

mining in retail industry helps in identifying customer

buying patterns and trends that lead to improved quality of

customer service and good customer retention and


B. Telecommunication Industry
Today the telecommunication industry is one of the most

emerging industries providing various services such as fax,

pager, cellular phone, internet messenger, images, e-mail,

web data transmission, etc. Due to the development of new

computer and communication technologies, the

telecommunication industry is rapidly expanding. This is the

reason why data mining is become very important to help

and understand the business. Data mining in

telecommunication industry helps in identifying the

telecommunication patterns, catch fraudulent activities,

make better use of resource, and improve quality of service.

C. Education
There is a new emerging field, called Educational Data

Mining, concerns with developing methods that discover

knowledge from data originating from educational

Environments. The goals of EDM are identified as predicting

students’ future learning , studying the effects of educational

support, and advancing scientific knowledge about


Data mining can be used by an institution to take accurate

decisions and also to predict the results of the student. With

the results the institution can focus on what to teach and

how to teach. Learning of the students can be captured and

used to develop techniques to teach them.

Customer Relationship Management is all about acquiring

and retaining customers, also improving customers’ loyalty


International Journal of Trend in Scientific Research and Development (IJTSRD) @ eISSN: 2456-6470

@ IJTSRD | Unique Paper ID – IJTSRD31368 | Volume – 4 | Issue – 4 | May-June 2020 Page 1334

and implementing customer focused strategies. To maintain

a proper relationship with a customer a business need to

collect data and the information. This is where data mining

plays its part. With data mining technologies the collected

data can be used for analysis. Instead of being confused

where to focus to retain customer, the seekers for the

solution get filtered results.

E. Fraud Detection
Billions of dollars have been lost to the action of frauds.

Traditional methods of fraud detection are and complex.

Data mining aids in providing meaningful patterns and

turning data into information. Any information that is valid

and useful is knowledge. A perfect fraud detection system

should protect information of all the users. A supervised

method includes collection of sample records. These records

are classified fraudulent or non-fraudulent. A model is built

using this data and the algorithm is made to identify whether

the record is fraudulent or not.

F. Intrusion Detection
Any action that will compromise the integrity and

confidentiality of a resource is an intrusion. The defensive

measures to avoid an intrusion includes user authentication,

avoid programming errors, and information protection. Data

mining can help improve intrusion detection by adding a

level of focus to anomaly detection. It helps an analyst to

distinguish an activity from common everyday network

activity. Data mining also helps extract data which is more

relevant to the problem.

The data mining process is divided into two parts i.e. Data

and Data Mining. Data involves data cleaning, data

integration, data reduction, and data transformation. The

data mining part performs data mining, pattern evaluation

and knowledge representation of data.

A. Data Cleaning
Data cleaning is the first step in data mining. It holds

importance as dirty data if used directly in mining can cause

confusion in procedures and produce inaccurate results.

Basically, this step involves the removal of noisy or

incomplete data from the collection. Many methods that

generally clean data by itself are they are not robust.

B. Data Integration
When multiple heterogeneous data sources such as

databases, data cubes or files are combined for analysis, this

process is called data integration. This can help in improving

the accuracy and speed of the data mining process. Different

databases have different naming conventions of variables, by

causing redundancies in the databases. Additional Data

Cleaning can be performed to remove the redundancies and

inconsistencies from the data integration without affecting

the reliability of data.

C. Data Reduction
This technique is applied to obtain relevant data for analysis

from the collection of data. The size of the representation is

much smaller in volume while maintaining integrity. Data

Reduction is performed using methods such as Naive Bayes,

Decision Trees, Neural network, etc.

D. Data Transformation
In this process, data is transformed into a form suitable for

the data mining process. Data is consolidated so that the

mining process is more efficient and the patterns are easier

to understand. Data Transformation involves Data Mapping

and code generation process.

E. Data Mining
Data Mining is a process to identify interesting patterns and

knowledge from a large amount of data. In these steps,

intelligent patterns are applied to extract the data patterns.

The data is represented in the form of patterns and models

are structured using classification and clustering techniques.

F. Pattern Evaluation
This step involves identifying interesting patterns

representing the knowledge based on measures. Data and

visualization methods are used to make the data

understandable by the user.

G. Knowledge Representation
Knowledge representation is a step where data visualization

and knowledge representation tools are used to represent

the mined data. Data is visualized in the form of reports,

tables, etc.

A. Flat files:
Flat files is defined as data files in text form or binary form

with a structure that can be easily extracted by data mining

algorithms. Data stored in flat files have no relationship or

path among themselves, like if a relational database is stored

on flat file, then there will be no relations between the tables.

Flat files are represented by data dictionary.

B. Relational Database:
A Relational database is defined as the collection of data

organized in tables with rows and columns. Physical schema

in Relational databases is a schema which defines the

structure of tables. Logical schema in Relational databases is

a schema which defines the relationship among tables.

C. Data Warehouses:
A is defined as the collection of data integrated from multiple

sources that will and There are three types of: Enterprise,

Data Mart and Virtual Warehouse. Two approaches can be

used to update data in Data Warehouse: Query-driven

Approach and Update-driven Approach.

International Journal of Trend in Scientific Research and Development (IJTSRD) @ eISSN: 2456-6470

@ IJTSRD | Unique Paper ID – IJTSRD31368 | Volume – 4 | Issue – 4 | May-June 2020 Page 1335

D. Databases:
databases is a collection of data organized by time stamps,

date, etc to represent transaction in databases. This type of

database has the capability to roll back or undo its operation

when a transaction is not completed or committed. It is

highly flexible system where users can modify information

without changing any sensitive information.

E. Multimedia databases:
Multimedia databases consists audio, video, images and text

media. They can be stored on Object-Oriented Databases.

They are complex information in a formats.

F. Spatial Databases:
Spatial databases store geographical information. can store

data in the form of coordinates, topology, lines, polygons, etc.

G. Time Series Databases:
Time series databases contains stock exchange data and user

logged activities. handle array of numbers indexed by time,

date, etc. It requires real-time analysis.

WWW refers to World wide web which is a collection of

documents and resources like audio, video, text, etc which

are identified by Uniform Resource Locators (URLs) through

web browsers, linked by HTML pages, and accessible via the

Internet network. It is the most heterogeneous repository as

it collects data from multiple resources. It is dynamic in

nature as volume of data is continuously increasing and


Data mining is highly effective and some techniques used for

data mining are as follows:

This analysis is used to retrieve important and relevant

information about data, and metadata. It is used to classify

different data in different classes. Classification is similar to

clustering in a way that it also segments data records into

different segments called classes. But unlike clustering, here

the data analysts would have the knowledge of different

classes or cluster. So, in classification analysis you would

apply algorithms to decide how new data should be


It refers to the method that can help you identify some

interesting relations (dependency modeling) between

different variables in large databases. This technique can

help you unpack some hidden patterns in the data that can

be used to identify variables within the data and the

concurrence of different variables that appear very

frequently in the . rules are useful for examining and

forecasting customer behavior. It is highly recommended in

the retail industry analysis. This technique is used to

determine shopping basket data analysis, product clustering,

catalog design and store layout. In IT, programmers use

association rules to build programs capable of machine


This refers to the observation for data items in a that do not

match an expected pattern or an expected behavior.

Anomalies are also known as outliers, novelties, noise,

deviations and exceptions. Often they provide critical and

actionable information. An anomaly is an item that deviates

considerably from the common average within a or a

combination of data. These types of items are statistically

aloof as compared to the rest of the data and hence, it

indicates that something out of the ordinary has happened

and requires additional attention. technique can be used in a

variety of domains, such as intrusion detection, system

health monitoring, fraud detection, fault detection, event

detection in sensor networks, and detecting disturbances.

Analysts often remove the anomalous data from the top

discover results with an increased accuracy.

The cluster is actually a collection of data objects; those

objects are similar within the same cluster. That means the

objects are similar to one another within the same they are

rather they are dissimilar or unrelated to the objects in other

groups or in other clusters. Clustering analysis is the process

of discovering groups and clusters in the data in such a way

that the degree of association between two objects is highest

if they belong to the same group and lowest otherwise. result

of this analysis can be used to create customer profiling.

In statistical terms, a regression analysis is the process of

identifying and analyzing the relationship among variables.

It can help you understand the characteristic value of the

dependent variable changes, if any one of the independent

variables is varied. This means one variable is dependent on

another, but it is not vice versa. is generally used for

prediction and forecasting.


There are several types of benefits and advantages of data

mining systems. Some of them are as follows:

� One of the common benefits that can be derived with
these data mining systems is that they can be helpful

while predicting future trends. And that is quite possible

with the help of technology and behavioral changes

adopted by the people.

� Data mining helps organizations to make the profitable
adjustments in operation and production.

� The data mining is a cost-effective and efficient solution
compared to other statistical data applications.

� Most parts of the data mining process is basically from
information gathered with the help of marketing

analysis. With the help of such marketing analysis, one

can also find out those fraudulent acts and products

available in the market. Moreover, with the help of it one

can understand the importance of accurate information.

� It can be implemented in new systems as well as existing
platforms. is the speedy process which makes it easy for

the users to analyze huge amount of data in less time.

Data mining technology is something that helps one person

in their and that is a process wherein which all the factors of

mining is involved precisely and while the involvement of

these mining systems, one can come across several

disadvantages of data they are as follows:

� There are chances of companies may sell useful
information of their customers to other companies for


International Journal of Trend in Scientific Research and Development (IJTSRD) @ eISSN: 2456-6470

@ IJTSRD | Unique Paper ID – IJTSRD31368 | Volume – 4 | Issue – 4 | May-June 2020 Page 1336

� Many data mining analytics software is difficult to
operate and requires advance training to work on.

� Different data mining tools work in different manners
due to different algorithms employed in their design.

Therefore, the selection of correct data mining tool is a

very difficult task.

� The data mining techniques are not accurate, and so it
can cause serious consequences in certain conditions.

Data Mining is an iterative process where the mining process

can be refined, and new data can be integrated to get more

efficient results. Data Mining meets the requirement of

effective, and flexible data analysis. It can be considered as a

natural evaluation of information technology. As a

knowledge discovery process, data preparation and data

mining tasks complete the data mining process. Data mining

processes can be performed on any kind of data discussed in

the above section. Finally, the bottom line is that all the

techniques help in the discovery of new creative things. At

the end of this paper about data mining, one can clearly

understand the areas of applications, types of source data,

process, techniques, and benefits with its own limitations.

Therefore, after reading all the above-mentioned

information about data mining one can determine its

credibility and feasibility even better.


[1] “Data Mining Curriculum”. ACM SIGKDD. 2006-04-30.
Retrieved 2014-01-27.

[2] ^ Clifton, Christopher (2010). “Encyclopædia
Britannica: Definition of Data Mining”. Retrieved 2010-


[3] ^ Hastie, Trevor; Tibshirani, Robert; Friedman, Jerome
(2009). “The Elements of Statistical Learning: Data

Mining, Inference, and Prediction”. Archived from the

original on 2009-11-10. Retrieved 2012-08-07

[4] ^ Han, Kamber, Pei, Jaiwei, Micheline, Jian (June 9,
2011). Data Mining: Concepts and Techniques (3rd

ed.). Morgan Kaufmann. ISBN 978-0-12-381479-1.

[5] Kantardzic, Mehmed (2003). Data Mining: Concepts,
Models, Methods, and Algorithms. John Wiley & Sons.

ISBN 978-0-471-22852-3. OCLC 50055336.

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