This document discusses data mining. It begins by defining data mining as a process used to extract useful and predictive data from large databases. It then discusses the uses of data mining in fields like banking, finance, retail, business, and healthcare. Finally, it outlines some of the main methods of data mining, including classification, clustering, and sequential pattern analysis, and discusses the advantages and disadvantages of data mining.

1. - SAGAR DAHAL

2.  INTRODUCTION
 USES OF DATA MINING
OBJECTIVES OF DATA MINING
EVOLUTION OF DATA MINING
METHODS OF DATA MINING
ADVANTAGES AND DISADVANTAGES

3. WHAT IS DATA MINING?
It is defined as a process used to extract useful and predictive data from the large
data bases.
It is a powerful technology with great potential to help companies focus on the
collection of most important datas from their data bases.
It can also be called as “ probably the answer of every question”.
It uses various algorithms , multiprocessor computers, massive databases to
analyze the trends.

4. SUBJECTS RELATED TO DATA
MINING :

5. INTERCONNECTION OF DATA
MINING:

6. USES OF DATA MINING :
Data mining is used in the various fields such
as:
Bank &Finance Retail
Business Health care
Data mining
… any many more

7. WHAT DATA MINING CAN DO FOR
YOU?
Improve customer service
Identify high-risk clients and transactions
Identify frauds and criminal activities
Identify business trends and patterns
Predict the future of the company by
analyzing past informations

8. EVOLUTION OF DATA
MINING
•It all started with the need to store the data in computers and improve the
access to it for decision-making.
•At the beginning of 1960s, the data was collected for the purpose of making
simple calculations to answer the business questions like the total average
revenue for a specific period of time.
•Further ,on the years of 80s and 90s , the usage of data warehouses to store
data in a structured format emerged.
•On coming to the year of 2000s , it was developed at the peak as it was able
to answer the futuristic questions using complex algorithms, massive data
bases.

9. DATA MINING PROCESS

10. DATA MINING METHODS
1. Classification:
Classification is used to classify each item in a set of data into
one of a predefined set of classes or groups.
2. Clustering:
Clustering is a data mining technique that makes a meaningful
or useful cluster of objects which have similar characteristics
using the automatic technique.
3. Sequential Patterns:
Sequential patterns analysis is one of data mining technique
that seeks to discover or identify similar patterns, regular events or
trends in transaction data over a business period.

11. ADVANTAGES OF DATA MINING
Predict future trends, customer purchase habits
Help with decision making
Improve company revenue and lower costs
Market basket analysis
Fraud detection

12. DISADVANTAGES OF DATA MINING
 Misuse of user privacy/security
Amount of data is overwhelming
Great cost at implementation stage
 Possible misuse of information
 Possible inaccuracy of data