In of the consumers credit card accounts. Even

In today’s world, the data which is
generated is growing exponentially. Every time you swipe your card to make payment,
you are actually generating data and that data is getting stored in some database.
On most transactions that you do there is some sort of data download.
Organizations are storing, processing, and analysing data. Data mining uses
mathematical methods that may include mathematical equations and algorithms. Wherever
there is data, data mining shall be used. This may be used in various industries
be it healthcare, or manufacturing, or any other field which involves data. The
data mining tools allow users to predict future trends. It involves machine
learning, statistics and database systems.

Data mining is a technology with which the
user is able to extract hidden predictive information from large databases.
This allows the user and businesses and the user to take proactive and calculated
decisions. It extracts relevant data from large databases, data warehouses and
other storing tools.

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Data mining can be used in various sectors
such as Marketing, Finance, Healthcare, Transportation and other areas.

Let us try to understand data mining with
an example. Ever wondered what do e-commerce giants such as amazon do in order
to predict other items which you may like and are showcased to you in the “recommended
for you” section. Well, the answer to that is Data Mining. The same technique
is used by credit card issuer Capital One which generates for its customer service
representative a list of products and services that a consumer is likely to buy
based on the characteristics of the consumers credit card accounts. Even the
National Basketball Association(NBA) is exploring a data mining application that
can be used in conjunction with image recordings of basketball games. The
advanced stout software shall analyse the movement of players and this shall
help the coach to make appropriate strategies.               

Below I have mentioned some data techniques
which are as follows:

1.      
Association

2.      
Classification

3.      
Clustering

4.      
Regression analysis

5.      
Prediction

6.      
Decision trees

7.      
Sequential Patterns