Download ASSOCIATON RULE MINING ASSOCIATON RULE

ASSOCIATON RULE MINING
ASSOCIATON RULE MINING ON OLAP CUBE
ON OLAP CUBE
Engin MADEN
[email protected]
AGENDA








Data Mining (Definition and Applications)
OLAP:Online Analytical Processing
Main Types of OLAP(ROLAP,MOLAP,HOLAP)
Multidimensional Database
OLAM:Online Analytical Mining
Association Rule Mining
Applying Association Rule Mining on OLAP Cube
C
Conclusion
l i
2
Data Mining
Data Mining (Definition)
Data Mining 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.
g,
,
y
3
Data Mining
Data Mining
(Applications)
 Business: Analysis of historical business activities
 Science and Engineering:
Bi i f
Bioinformatics,Genetics,Medicine,Education
ti G
ti M di i
Ed
ti
etc.
t
 Human Rights: Discovery of systematic human rights
violations
 Spatial Data Mining: Find patterns in data with
respect to geography
4
OLAP
• An approach to answer multi-dimensional analytical
(MDA) queries swiftly.
swiftly
• Typical Applications
o
o
o
o
o
o
Business reporting
p
g for sales
Marketing
Management reporting
B siness process management (BPM)
Business
Budgeting and forecasting
Financial reporting
5
Main Types of OLAP:
ROLAP
•
•
•
•
Relational Online Analytical
Processing
Base data and dimension
tables are stored as
relational tables
Permits
i multidimensional
i i
i
analysis of data
Does not require the pre
precomputation and storage of
information
6
Main Types of OLAP:
MOLAP
•
•
•
Multidimensional Online Ana
lytical Processing
More traditional way of
OLAP analysis.
Data is
i stored iin a
multidimensional cube, not
in a relational database.
7
Main Types of OLAP:
HOLAP
•
•
•
•
Hybrid Online Analytical Pro
cessing
Mixture of MOLAP and
ROLAP
Bridges the technology gap
of both products
Enables access and use of
both MDDB and RDBMS
data stores
8
Multidimensional Database
•
•
•
A partt off OLAP tto allow
ll
the
th efficient
ffi i t storage
t
and
d retrieval
ti
l off
large volumes of data
Data is viewed and analyzed
y
from different p
perspectives:
p
Dimensions
Dimension Tables and Fact Tables
9
OLAM: Online Analytical Mining
•
•
•
Applies mining techniques on
OLAP cube
Also called OLAP Mining
Integrates OLAP with Data
Mining
10
Association Rule Mining
• A kind
ki d off d
data mining
i i
technique
h i
that
h discovers
di
interesting patterns and correlations between data
• Frequent itemstes and frequent patterns
• Association Rule: Antecedent (IF) + Consequent (THEN)
• Support & Confidence
11
Association Rule Mining On OLAP Cube
• Dimension Tables: Student, College and
Zone
• Fact Table: University
• Star Schema
• OLAP Cube is made with the help of SQL
S
Server
analysis
l i service
i
12
Association Rule Mining On OLAP Cube (cont.)
M ltidi
Multidimensional
i
ld
database
t b
di
display
l
with
ith star
t schema
h
13
Association Rule Mining On OLAP Cube (cont.)
•
•
OLAP Cube:
C b CPI and
d SPI are measure attributes
tt ib t
Browsing OLAP cube according to measure
14
Association Rule Mining On OLAP Cube (cont.)
•
Apply
A
l association
i ti
rule
l mining
i i
on OLAP d
data
t cube
b and
d
find the frequent items (min_support:4)
15
Association Rule Mining On OLAP Cube (cont.)
16
Conclusion
• OLAP can not give the relationship between data
• OLAP Mining combines OLAP with data mining
t h i
techniques
• OLAM uses association rule mining method on OLAP
cubes
• OLAM gives frequent items and rules of the data in
OLAP cubes
17
References
• Association Rule Mining Method On OLAP Cube:
Jigna J. Jadav, Mahesh Panchal (International
Journal of Engineering Research and Applications
(IJERA) ISSN: 2248-9622 Vol. 2, Issue 2,Mar-Apr 2012,
pp.1147-1151 )
pp
• Data mining - Concept and Techniques, Jiawei Han
& Micheline Kamber
• http://thebusinessintelligence.blogspot.com/2009/1
2/online-analytical-processing-olap.html
18