horizonntal distrubution
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A MAIN-PROJECT SUBMITTED TOCOMPUTER SCIENCE AND SYSTEMS ENGINEERING DEPTARTMENT
BYJ.MOUNICA 09121A1522Y.VISHNU KUMARI 09121A1553J.SAI SUDHA 09121A1519
Under the guidance of Ms. P. DHANA LAKSHMIAssistant professor
Department of CSSE
Department of Computer Science and Systems EngineeringSREE VIDYANIKETHAN ENGINEERING COLLEGE (AUTONOUMS)
(Affiliated to JNTU, Anantapur, Approved by AICTE, New Delhi, Accredited by NBA)Sree Sainath Nagar, A.Rangampet-517102
Chittoor Dist, Andhra Pradesh2009-2013.
HORIZONTAL DISTRIBUTED ASSOCIATION RULE MININGFOR PARALLEL DISTRIBUTED DATA SETS
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AGENDA
Introduction Statement of the problem Objectives Scope Literature Survey Rival methods S/W & H/W requirements Physical model Mathematical model
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INTRODUCTIONData mining is a technology to explore, analyze and finally
discovering patterns from large data repository. Associationrule mining, a technique in data mining is used to describerelations among items in transactions. Two types of database
environments exist namely centralized and distributed. Incontrast to the centralized data base model, the distributed database model assumes that the data base is partitioned intodisjoint fragments and each fragment is assigned to one site. Incentralized environment, sequential approach is adopted
whereas in distributed environment parallel approach isfollowed.
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Statement of the problemTo provide security in distributed systems in order to
explore useful information .
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Objectives To provide security in distributed data mining To reduce cost concerns To provide time efficiency
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ScopeA hash based secure sum cryptography technique is used to
find the global association rules by preserving the privacyconstraints.
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Literature Survey Fast algorithm for mining association rules in large
databases by R. Agrawal, R. Srikanth A new approach to online generation of association rules by
Charu C.Agrawal,Philip S Mining association rules between sets of items in large data
bases by R.Agrawal , T.Swami Mining association rules with item set constraints by
Ramakrishna Srikanth, Rakesh Agrawal
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Rival methods Graph theoretic search algorithm Apriori algorithm Fast algorithms for mining association rules
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Graph Theoretic Search Approach
In this approach concept of adjacency lattice has been used,here the adjacency lattice could be stored either in primarymemory or secondary memory. The idea of adjacency is to pre-store a number of item sets at a level of support in a special
format called adjacency lattice.
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Adjacency Lattice
A B C
AB
AC
BC
ABC
ABC
AB
AC
BC
B CA
Lattice structureGraph for rule generation
0.8 0.8 0.6
0.60.8
0.4
0.670.67
0.67
0.75 0.750.75
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Apriori Algorithm
o Generates set of candidate item setso Prunes all infrequent candidates
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Market-basket analysis
TID ITEMS
I1 CHOCOLATE,MEAT,JUICE
I2 CHOCOLATE,CHEESE
I3 CHEESE,CLIPS
I4 CHOCOLATE,MEAT,CHEESE
I5 CHOCOLATE,CHEESE,MEAT,CLOTHES,JUICE
I6 MEAT,CLOTHES,JUICEI7 MEAT,JUICE,CLOTHES,CLIPS
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1 ITEMSET COMBINATION
Minimum support=3
ITEMS SUPPORTCHOCOLATE 4
JUICE 4
CHEESE 4
CLOTHES 3CLIPS 2
MEAT 5
ITEMS SUPPORT
A=CHOCOLATE 4
B=JUICE 4
C=CHEESE 4
D=CLOTHES 3
F=MEAT 5
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2 ITEMSET COMBINATION
Minimum support : 3
ITEMS SUPPORT
AB 2
AC 3AD 1
AF 3
BC 1
BD 3
BF 4
CD 1
CF 2
DF 3
ITEMS SUPPORT
AC 3
AF 3
BD 3BF 4
DF 3
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3 ITEMSET COMBINATION
Here only 1 item set meets the requirement of support.
ITEMS SUPPORT
BDF 3
RULES GENERATED:
BD=>FBF=>DDF=>B
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Fast Algorithm for Mining Association rules
Apriori hybrid AIS SETM
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H/W Requirements: A CPU with Core2 duo 2GB RAM 80GB HDD Guest OS(Windows XP, Window 7, Linux)
S/W Requirements: OS should have JRE(Java Runtime Environment) Language: JAVA IDE: Netbeans 6.5 Back end:DB2 Build tool: ANT
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Physical model
Parallel distributed data sets
Branch1
Branch2
Branch3
Branch4
Globalfrequent item
sets
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Mathematical model
Privacy Preserving Distributed Item set mining process
Actual support = GE + minimum support * total database size
Rule Generation Process
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Secure Hash Standard
This standard specifies a secure hash algorithm, SHA-1for computing a condensed representation of a message.
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Signature Generation
SHA-1
message
Message digest
DSA signOperation
Private digital
key signature
SHA-1
Received message
Message digest
DSA signOperation
digital public
signature key
Sign verification
Signature verification
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ITEMS SUPPORT
A 7
B 4
C 2
D 1
ITEMS SUPPORT
B 5
C 3
D 2
F 4
ITEMS SUPPORT
A 7
B 9
C 5
F 4
Distributed Item sets
Global Item set
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