1. Guided by, M.R.Vinu
Ms. Nandhini. M, 11MZ36
Asst Professor(Sr. Gr), II M.E CSE
Dept of CSE, Dept of CSE
PSG College of Technology. PSG College of Technology.

2. Agenda
 Objective
 Problem Definition
 Literature Survey
 Need for Current Study
 Methodology
 Scope of the Project
 Timeline of Activities
 Major References

3. What is Association Rule Mining and
Weighted Association rule mining?
 Association rule mining is popular method, to mine
association rules that satisfy the predefined minimum
support and confidence from a given database.
 In weighted association rule mining, weight are
assigned to each item to reflect interest of each item
within the transaction.

4. OBJECTIVES
 To improve the confidence of the rule using
pre assigned weights for each item/attribute.
 To improve the quality of the rule by reducing the
quantity of the rules without compromising the
usefulness factor.
 To improve the computational efficiency of rule
mining.

5. LITERATURE SURVEY

6. Association Rule Induction
Apriori algorithm is the best known algorithm to mine
association rules but ends up with many disadvantages
 The algorithm does not return result in a reasonable time.
 It only tells the presence and absence of an item in
transactional database.
 It is not efficient in case of large dataset(dense dataset).
 ARM fails to associate subjective measures.

7. Contd..
 So, the traditional model of association rule mining is
adapted to handle weighted association rule mining.
 This algorithm is both scalable and efficient in
discovering significant relationships but still ends up in
long frequent pattern for dense data.
 This problem does not require changes in the existing rule
mining ,therefore it is necessary to apply during post-
mining of association rules.

8. PROBLEM DEFINITION
• It is difficult for the users to find interesting
association rule as there are more number of
association rules discovered before.
• Therefore, it is important to remove insignificant rules
and prune redundancy as well as
summarize, visualize, and post-mine the discovered
rules

9. METHODOLOGY

10. SCOPE OF THE PROJECT
 Generating Weighted Association Rules
 Creating ontology for the Datasets(Census Income
Dataset)
 Generating Rule Schemas
 Applying Operators over rule schemas
 Applying Filters over rules schemas
 Comparing efficiency after applying operators and
Filters

11. NEED FOR CURRENT STUDY
 Weighted concept is used in order to gain a more
realistic mining result.
 Weighted Association Rules can filter out the minor
rules, extracts the implicit and useful rules for users.

12. TIMELINE ACTIVITIES

13. Activities June July August September
I II III IV I II III IV I II III IV I II III IV
Literature
review, Data
collection.
Creating
Ontology for
the dataser
Generating
Rule
Schemas
Applying
Operators over
rule schemas
and Filters
over rules
schemas
Comparing
efficiency
Work planned Work done Work in progress

14. MAJOR REFERENCES
 C.H.Cai ,Ada W.C.Fu ,C.H.Cheng and W.W. Kwong “Mining
Association Rules with weighted Itemset”.In:Department of computer
science and Engineering The Chinese University of Hong Kong
 R. Natarajan and B. Shekar, “A Relatedness-Based Data-Driven
Approach to Determination of Interestingness of Association Rules,”
Proc. 2005 ACM Symp. Applied Computing (SAC), pp. 551-552, 2005.
 R.J. Bayardo, Jr., R. Agrawal, and D. Gunopulos, “Constraint-Based Rule
Mining in Large, Dense Databases,” Proc. 15th Int’lConf. Data Eng.
(ICDE ’99), pp. 188-197, 1999.

15. Contd…
 Wei Wang,Jiong Yang,Philip S.Yu “Efficient Mining Of
weighted Association Rules”,In:ACM 2000
 Claudia Marinica anf Fabricegillet,june2010,”Knowlede
–Based interactive Postmining of Association Rules
using Ontologyies”.In :IEEE Transaction on knowledge
and Data Engineering

16. THANKYOU