1. Data Mining
Data mining (the analysis step of the "Knowledge Discovery in
Databases" process, or KDD),an interdisciplinary subfield
of computer science, is the computational process of discovering
patterns in large data sets involving methods at the intersection
of artificial intelligence, machine learning, statistics, and database
systems
2. What is Data Mining?
Data Mining, also known as Knowledge-
Discovery in Databases (KDD), is the process
of automatically searching large volumes of
data for patterns.
Data Mining applies many older
computational techniques from statistics,
machine learning and pattern recognition
3. Data mining consists of five
major elements:
Extract, transform, and load transaction data
onto the data warehouse system.
Store and manage the data in a
multidimensional database system.
Provide data access to business analysts and
information technology professionals.
Analyze the data by application software.
Present the data in a useful format, such as a
graph or table.
4. Data Mining Goal
The ultimate goal of data mining is
prediction - and predictive data mining
is the most common type of data mining
and one that has the most direct
business applications.
5. 3 Steps Data Mining Process
Stage 1: Exploration. This stage usually starts
with data preparation which may involve cleaning
data, data transformations, selecting subsets of
records
Stage 2: Model building and validation. This
stage involves considering various models and
choosing the best one based on their predictive
performance
Stage 3: Deployment. That final stage involves
using the model selected as best in the previous
stage and applying it to new data in order to generate
predictions or estimates of the expected outcome
6. Some of the tools used for
data mining are:
Artificial neural networks - Non-linear predictive models that
learn through training and resemble biological neural networks
in structure.
Decision trees - Tree-shaped structures that represent sets of
decisions. These decisions generate rules for the classification
of a dataset.
Rule induction - The extraction of useful if-then rules from data
based on statistical significance.
Genetic algorithms - Optimization techniques based on the
concepts of genetic combination, mutation, and natural
selection.
Nearest neighbor - A classification technique that classifies
each record based on the records most similar to it in an
historical database.
7. Reasons for the growing
popularity of Data Mining
Growing Data Volume
Limitations of Human Analysis
Low Cost of Machine Learning
8. ADVANTAGES OF DATA
MINING
Marking/Retailing: Data mining can
aid direct marketers by providing them
with useful and accurate trends about
their customers’ purchasing behavior.
Banking/Crediting: Data mining can
assist financial institutions in areas such
as credit reporting and loan
information.
9. ADVANTAGES OF DATA
MINING Cont…
Law enforcement: Data mining can aid law
enforcers in identifying criminal suspects as
well as apprehending these criminals by
examining trends in location, crime type,
habit, and other patterns of behaviors.
Researchers: Data mining can assist
researchers by speeding up their data
analyzing process; thus, allowing them more
time to work on other projects.
10. DISADVANTAGES OF
DATA MINING
Privacy Issues: For example,
according to Washing Post, in 1998,
CVS had sold their patient’s
prescription purchases to a different
company
American Express also sold their
customers’ credit card purchases to
another company.
11. DISADVANTAGES OF
DATA MINING Cont…
Security issues: Although companies have a lot of
personal information about us available online, they
do not have sufficient security systems in place to
protect that information.
Misuse of information: Some of the company will
answer your phone based on your purchase history.
If you have spent a lot of money or buying
a lot of product from one company, your call will be
answered really soon. So you should not think that
your call is really being answer in the order in which it
was receive.
