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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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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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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