Presentation data mining

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PUNJAB UNIVERSITY COLLEGE OF
INFORMATION TECHNOLOGY

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Data Mining
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Abdul Basit khan
Ali Rehman
Haseeb Ahmed
Saira Bano
Iftikhar Ahmed
G.Farid Razzaqi
Group Members
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Some Terms
•Data raw facts and figures
•Information pattern or relationship
among data
•Information can be converted into
Knowledge
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Finding VS Discovery
you find something that you know you
have lost, or you know that something
exist but not in your approach so you
search for that and find.
In the other hand, you don’t know the
results, so the term discovery rather than
finding is used. Coulumbus discovered
America..
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Before we go to Data Mining
Things around us occure such as:
1. Known knowns
2. Known unknowns
3. Unknown unknowns
4. Unknown knowns
In DM we are concerned with
the-----one!!!
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“finding a needle in a haystack..”
It is a tough job to find a needle in a big
box full of hay.
Data mining is like finding in the haystack
(huge data) the needle (knowledge)…
Where you don’t know the idea about
where the needle can be found ?
A Quote
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Data Mining
•Non-trivial extraction of previously unknown and
potentially useful information from data in large
DATABASE
•Data mining discovers hidden information in your
data, but it cannot tell you the value of the
information to your organization.
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Non-trivial (not common)
It is not a straightforward
computation of predefined quantities
like computing the average value of a
set of numbers. .e.g. Suwaiyan on
eid..
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Goal of data mining
Extract information from a data
set and transform it into an
understandable structure for
further use
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History
•The term "Knowledge Discovery in
Databases“ 1989 popular in AI and
Machine Learning Community.
•The term "Data Mining" appeared
around 1990 in the Database
community.
Now a day
•Data Mining and Knowledge
Discovery are used interchangeably.
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Early methods of identifying patterns in data
Bayes' theorem (1700s) and regression
analysis (1800s) neural networks, cluster
analysis, genetic algorithms (1950s), decision
trees (1960s), and support vector machines
(1990s).
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Spatial Data Mining
 Spatial data mining is the
application of data mining methods
to spatial data. The end objective
of spatial data mining is to find
patterns in data with respect to
geography.
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•offices requiring analysis or dissemination of
geo-referenced statistical data
•public health services searching for
explanations of disease clustering
•environmental agencies assessing the impact
of changing land-use patterns on climate
change
•geo-marketing companies doing customer
segmentation based on spatial location.
EXAMPLES
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Data Claude Shannon’s info. theory
• More volume of data means less
information
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Examples: What is (not) Data Mining?
 What is not Data
Mining?
– Look up phone
number in phone
directory
– Query a Web search
engine for information
about “Amazon”
 What is Data Mining?
– Certain names are more
prevalent in certain US
locations (O’Brien,
O'Rourke, O’Reilly… in
Boston area)
– Group together similar
documents returned by
search engine according to
their context (e.g. Amazon
rainforest, Amazon.com,)
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Data mining VS statistics
(Both Deal With Data Analysis)
1. Data mining is
knowledge driven
2. Data mining is
discovery driven i.e.
patterns and
hypothesis are
automatically
extracted from data.
3. Data mining builds
many complex,
predictive, nonlinear
models
1. statistics is human
driven.
2. inference is
assumption driven
i.e. a hypothesis is
formed and
validated against
the data.
3. statistics focuses on
small data sets
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Data Data Mining process
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Techniques
•Prediction
•Description
•Classification
•Market basket analysis
•Estimation
•Clustering
•Decision tree
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Prediction
Records are classified according to some
predicted future behavior.
It is not like the prediction that a palmist
do..
Example:
Predicting that, “how much” a customer
will spend during the next six months?
For this the customers…, their interests,
their likes and dislikes, their buying
patterns should be understood
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Description
It describe what is going on in the complicated data base to
increase our under standing
example
some standard tools are 1 summary statistics (center It
describe what is going on in the complicated data base to
increase our under standing
Example:
some standard tools are
summary statistics (center tendency i.e. average, mean,
median, mode or
measure of scattering i.e. range)
Graphical representation (histograms, graphs, plots
tendency or measure of scattering)
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Classification
Based on the properties of existing data we assign
the new data element to either of the groups
Example:
In a News Web Site, to make a decision that at
what place a specific news will be placed in!!!
The following questions should be determined:
What should be the news chapter category?
What should be its hierarchical position?
Should it be placed in sports or weather section?
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Answer
Manually it is time consuming and prone
to errors.
Classification techniques are used to scan
and process the documents
e.g. the frequent occurrence of the key
word “cricket” will help to place in a
specific category that is “SPORTS Section”
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CLASSIFICATION
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Market basket analysis
To know which items are sold together
Example: people often bought tea and
milk together from a shop
We can run sales-promotion campaign
by placing the things that are bought
together in near by places in the store
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Data Estimation
Building a model and assigning a value
from 0 to 1 to each member of the set.
This value tells the probability of a record
belonging to a group
Then classifying the members into
categories based on a threshold value.
As the threshold changes the class
changes
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Decision Trees
•Decision trees are tree-shaped
structures that represent decision
sets.
• These decisions generate rules,
which then are used to classify data.
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Data set for decision tree
height hair eyes class
short blond blue A
tall blond brown B
tall red blue A
short dark blue B
tall dark blue B
tall blond blue A
tall dark brown B
short blond brown B
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height hair eyes class
short blond blue A
tall blond brown B
tall red blue A
short dark blue B
tall dark blue B
tall blond blue A
tall dark brown B
short blond brown B

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Decision Trees (cont.)
hair
dark
red
blond
short, blue = B
tall, blue = B
tall, brown= B
{tall, blue = A} short, blue = A
tall, brown = B
tall, blue = A
short, brown = B
Completely classifies dark-
haired
and red-haired people
Does not completely classify
blonde-haired people.
More work is required
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Decision Trees:
Learned Predictive Rules
hair
eyesB
B
A
A
dark
red
blond
blue brown
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Decision Tree
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Clustering
It is one of the most important DM techniques
It involves the grouping of data items without
taking any human parametric input.
Types
1. One way clustering is when only data records
(rows) are used
2. Two way clustering is when both the rows and
columns are being used for clustering purpose.
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Land use: Identification of areas
of similar land use in an earth
observation database.
City-planning: Identifying groups
of houses according to their
house type, value, and
geographical location.
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Clustering
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Data Conclusion
Data mining is more than running some complex
queries on the data you stored in your database.
You must work with your data, reformat it, or
restructure it, regardless of whether you are
using SQL, document-based databases such as
Hadoop, or simple flat files. Identifying the
format of the information that you need is based
upon the technique and the analysis that you
want to do. After you have the information in
the format you need, you can apply the different
techniques (individually or together) regardless
of the required underlying data structure or data
set.
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References
•free-books-online.org/.../data.../a-brief-
introduction-to-data-mining-dm/
•Advanced Data Mining Techniques by David L.
Olson Dursun Delen
•Data Mining: Concepts and Techniques
Second Edition by Jawed Han
•University of Illinois at Urbana-Champaign
•Oracle data mining technique
•Wikipedia the free encyclopedia
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