1. Method for frequent itemset Generation. 2. Searching in a BFS Manner . 3. Represent the data in Vertical format.

1. A Vertical Representation inA Vertical Representation in
Frequent Item-set MiningFrequent Item-set Mining
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DR MANMOHAN SINGH
ITM UNIVERSE VADODARA GUJARAT INDIA

2. IntroductionIntroduction
• Data mining searching for knowledge (interesting patterns) in
data.
• Data Mining is a process of analyzing data from different
perspectives and summarizing it into useful information.
• There are many techniques of Data Mining such as
Classification, Clustering, Association rule , Regression etc.
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3. Association RuleAssociation Rule
 Two Approach:
Frequent Item Set Generation
Rule Generation
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4. Frequent Item SetFrequent Item Set
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Various types of Algorithm used in Frequent Item Set Generation

5. MotivationMotivation
 Frequent item set mining include application like
market basket analysis, modular fragment mining,
web link analysis etc.
 It aims at finding regularities in the shopping
behavior of customers of supermarkets, mail-order
companies, on-line shops etc.
 In frequent itemset still there are some issues; that it not handle
large amount of data, and real time analysis due to higher time
complexity. 5

6. ObjectiveObjective
 Improve the work efficiency, scalability, and handle the large
amount of dataset.
 Try to improve efficiency of existing algorithm.
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7. Existing System ArchitectureExisting System Architecture
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Existing System Architecture

8. Example Of Existing SystemExample Of Existing System
TID Items
1 Bread, Butter, Jam
2 Butter, Coke
3 Butter, Milk
4 Bread, Butter, Coke
5 Bread, Milk
6 Bread, Milk
7 Bread, Milk
8 Bread, Butter, Milk, Jam
9 Bread, Butter, Milk
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Item set TID Set
Bread 1,4,5,7,8,9
Butter 1,2,3,4,6,8,9
Milk 3,5,6,7,8,9
Coke 2,4
Jam 1,8
Horizontal Data Layout Vertical Data Layout
Frequent 1-item sets

9. Conti..Conti.. Min_sup =2Min_sup =2
Frequent 2-item setsFrequent 2-item sets
Item Set TID Set
{Bread, Butter} 1,4,8,9
{Bread, Milk} 5,7,8,9
{Bread, Coke} 4
{Bread, Jam} 1,8
{Butter, Milk} 3,6,8,9
{Butter, Coke} 2,4
{Butter, Jam} 1,8
{Milk, Jam} 8
9
Item Set TID Set
{Bread, Butter,
Milk}
8,9
{Bread, Butter, Jam} 1,8
Frequent 3- item sets

10. Problem DefinitionProblem Definition
 In Eclat algorithm it is working only limited dataset.
 Improve only scalability of the item set.
 It can not handle memory size.
 Eclat algorithm does not take full advantage of Apriori
property to reduce the number of candidate itemsets explored
during frequent itemset generation.
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11. ConclusionConclusion
 The survey of various frequent itemset algorithm is done with each having
advantages, disadvantages and limitations over different parameters.
 The main motivation for frequent item set generation to increase the efficiency
and scalability.
 We want to increase efficiency, scalability compare to Eclat algorithm or better.
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12. ReferencesReferences
[1] Shamila Nasreen, Muhammad Awais Azamb, Khurram Shehzad, Usman Naeem,
Mustansar Ali Ghazanfar “Frequent Pattern mining algorithm finding associated frequent
patterns for Data Streams: A Survey” 2014, Science Direct
[2] Xiaofeng Zheng a
, Shu Wang a*
“Study on the Method of Road Transport Management
Information Data mining Based on Pruning Eclat Algorithm and Map Reduce “2014,
Science Direct
[3] Zhigang Zhang, Genlin Ji*
, Mengmeng Tang “MREclat: an Algorithm for Parallel
Mining Frequent Item sets” 2013, IEEE
[4] Marghny H. Mohamed • Mohammed M. Darwieesh “Efficient mining frequent item sets
algorithm” 2013, Springer
[5] Dr. S.Vijayarani, Ms. P. Sathya “An Efficient Algorithm for Mining Frequent Item Sets
in Data Streams” 2013, International Journal of Innovative Research in Computer and
Communication Engineering
[6] Kan Jin “A new Algorithm for Discovering Association Rules” 2010, IEEE
[7] Mingjun Song, and Sanguthevar Rajasekaran “A Transaction Mapping Algorithm for
Frequent Item Sets Mining” Member, IEEE
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