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Data mining is the principle of extracting the
information from large amounts of data.
In other words…
 Data mining (knowledge discovery from data)
◦ Extraction of interesting patterns or knowledge from
huge amount of data
 Other names
◦ Knowledge discovery (mining) in databases (KDD),
knowledge extraction, data/pattern analysis etc.
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Data Mining is principle of extracting the
information from the large amount of data.
In other words, we can say that data mining is
mining the knowledge from data.
There is huge amount of data available in
Information Industry.
 Analysing this huge amount of data and
extracting useful information from it is
necessary.
 In other words,
In field of Information technology, we have
huge amount of data available that need to
be turned into useful information.
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Here is the list of steps involved in knowledge
discovery process:
Data Cleaning - In this step the noise and
inconsistent data is removed.
Data Integration - In this step multiple
data sources are combined.( It merges the
data
from
multiple
heterogeneous
data sources into a coherent data store.)
Data Selection - In this step relevant to the
analysis task are retrieved from the database.
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Data Transformation - In this step data are
transformed or consolidated into forms
appropriate for mining by performing
summary or aggregation operations.
Data Mining - In this step intelligent methods
are applied in order to extract data patterns.
Pattern Evaluation - In this step, data
patterns are evaluated.
Knowledge Presentation - In this step,
knowledge is represented
Knowledge Discovery (KDD) Process
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Data mining—core of
knowledge discovery
process
Pattern Evaluation
Data Mining
Task-relevant Data
Data Warehouse
Data Cleaning
Data Integration
Databases
Selection
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Data mining is the process of analyzing large
databases to find useful patterns (data or
info.)
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Data Mining deals with what kind of data to
be mined.
There are two kind of functions involved in
Data Mining, that are :
Descriptive
Classification and Prediction
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The descriptive function deals with general
properties of data in the database.
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Here is the list of descriptive functions:
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Class/Concept Description
Mining of Frequent Patterns
Mining of Associations
Mining of Correlations
Mining of Clusters
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Class/Concepts refers the data to be
associated with classes or concepts.
For example, in a company classes of items
for sale include computer and printers, and
concepts of customers include big spenders
and budget spenders. Such descriptions of a
class or a concept are called class/concept
descriptions.
◦ Characterization: provides a concise and
succinct summarization of the given
collection of data
◦ Example: The characteristics of customers
who spend more than $1000 a year at All
Electronics. The result can be a general
profile such as age, employment status or
credit ratings.
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Characterization: provides a concise and succinct
summarization of the given collection of data
Comparison: provides descriptions comparing
two or more collections of data.
Example: The user may like to compare the
general features of software products whose
sales increased by 10% in the last year with
those whose sales decreased by about 30% in
the same duration.
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Frequent Patterns :
As the name suggests patterns that occur
frequently in data.
It describes the specific pattern within the
data.
• Association Analysis: from marketing perspective, determining
which items are frequently purchased together within the same
transaction.
• Example: An example is mined from the (some store) All Electronic
transactional database.
buys (X, “Computers”)  buys (X, “software”) [Support = 1%,
confidence = 90% ]
◦ X represents customer
◦ confidence = 90% , if a customer buys a computer there is a 50%
chance that he/she will buy software as well.
◦ Support = 1%, means that 1% of all the transactions under
analysis showed that computer and software were purchased
together.
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Confidence indicates the number of times
the if/then statements have been found to be
true.
Support is an indication of how frequently the
items appear in the database.
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Association is a data mining function that
discovers the probability of the cooccurrence of items in a collection.
The relationships between co-occurring items
are expressed as association rules or corelations.
Note: In data mining, association rules are
useful for analysing and predicting customer
behavior.
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Cluster refers to a group of similar kind of
objects.
Cluster analysis refers to forming group of
objects that are very similar to each other but
are highly different from the objects in other
clusters.
The goal is to place records into groups
where the records in a group are highly
similar to each other and dissimilar to records
in other groups.
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Classification
Regression
Outlier Analysis (Deviation Detection)
Evolution Analysis
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Classification is the process of learning a
model that is able to describe different
classes of data.
Classification model can be represented in
various forms such as
 A decision tree
Customer renting
property> 2 years???
Customer age> 25
years???
Rent
property
Rent
property
Buy
property
Age?
Income??
Class A
Class C
Class B
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Regression is a data mining function that
predicts a number.
Age, weight, distance, temperature, income,
or sales could all be predicted using
regression techniques.
For example, a regression model could be
used to predict children's height, given their
age, weight, and other factors.
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Regression modeling has many applications
in
business planning,
financial forecasting,
time series prediction,
environmental modeling
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Outlier Analysis - The Outliers may be defined as
the data objects that do not comply with general
behaviour or model of the data available.
Such data objects, which are grossly different
from or inconsistent with the remaining set of
data, are called outliers.
The outliers may be of particular interest, such as
in the case of fraud detection, where outliers may
indicate fraudulent activity.
Thus, outlier detection and analysis is an
interesting data mining task, referred to
as outlier mining or outlier analysis.
Discovering the most significant changes in
data
 A data object that deviates significantly from
the normal objects as if it were generated by
a different mechanism
 Deviation detection are different from the
noise data
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◦ Noise is random error or variance in a measured variable
◦ Noise should be removed before outlier detection
Applications:
◦ Credit card fraud detection
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Data visualization: using graphical methods
to show patterns in data.
Data visualization systems help users
examine large volumes of data and detect
patterns visually
◦ Can visually encode large amounts of information
on a single screen
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Evolution Analysis - Evolution Analysis refers
to description and model regularities or
trends for objects whose behaviour changes
over time.
Example
◦ Stock market predictions: future stock prices
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Example: Time-series data. If the stock market
data (time-series) of the last several years
available from the New York Stock exchange
and one would like to invest in shares of high
tech industrial companies. A data mining study
of stock exchange data may identify stock
evolution regularities for overall stocks and for
the stocks of particular companies. Such
regularities may help predict future trends in
stock market prices, contributing to one’s
decision making regarding stock investments.
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Example: Stock Market
Predict future values
Determine similar patterns over time
Classify behavior
© Prentice Hall
36
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This information further can be used for
various applications such as
market analysis,
fraud detection,
customer retention,
production control,
science exploration etc.
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Customer Profiling - Data Mining helps to
determine what kind of people buy what kind of
products.
Identifying Customer Requirements - Data
Mining helps in identifying the best products for
different customers. It uses prediction to find the
factors that may attract new customers.
Cross Market Analysis - Data Mining
performs Association/correlations between
product sales.
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Data Mining is also used in fields of credit
card services and telecommunication to
detect fraud.
In fraud telephone call it helps to find
destination of call, duration of call, time of
day or week.
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Finance Planning and Asset Evaluation - It
involves cash flow analysis and prediction,
contingent claim analysis to evaluate assets.
Resource Planning - Resource Planning It
involves summarizing and comparing the
resources and spending.
Competition - It involves monitoring
competitors and market directions.
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Marketing/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.
Researchers: Data mining can assist
researchers by speeding up their data
analyzing process; thus, allowing them more
time to work on other projects.
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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.