This document discusses using itemset mining to improve customer relationship management (CRM) in a computer hardware company. It proposes using the Faster Utility Mining (FUM) algorithm to efficiently mine high utility itemsets from transaction data to gain insights into customer interests. The FUM algorithm handles duplicate itemsets and generates candidate itemsets using combination generation. It calculates the utility value of each candidate and outputs those above a minimum utility threshold. This approach could provide the company with more semantically meaningful insights into customer purchasing patterns compared to traditional frequent itemset mining approaches.

1. Itemset Based On Customer Interest to
Improve the CRM Business
Under The Guidance Of
Mr.A. Salman Ayaz
Assistant Professor
Department Of Computer Application
By
V. Divya Devi
1028019

2. Objective
• The main goal of Association Rule Mining are to find all frequent itemsets
and to build rules based of frequent itemsets.
• Customer Relationship Management is a comprehensive approach for
creating, maintaining and expanding customer relationships.
– External customer
– Internal customer
• External customer
– Those outside the organization who buy the goods and services
the organization sells.
• Internal customer
– A way of defining another group inside the organization whose
work depends on the work of your group. Therefore, they are your
“customers.” It’s our responsibility to deliver what they need so
they can do their jobs properly.

3. Objective
• CRM
– Internal Customer
• ORGANIZATION STRUCTURE
– Computer And Hardware Company
• TECHNOLOGY IMPLEMENTATION
– Data Mining
• FUM Algorithm

4. Problem Definition
• The frequent itemset only reproduce the statistical correlation
between items and it does not reflect the semantic importance of the
items.
• To overcome this limitation go far utiility based itemset mining
approach.
• HUI mining is a research area of utility based descriptive data
mining aimed at finding itemset that contribute most to the total
utility.
• The well known faster and simpler algorithm for mining HUI from
large transactional database is FUM.

5. EXISTING SYSTEM
• The goal of utility mining is to discover all the itemsets whose utility values are
beyond a user specified threshold in a transaction database.
– Local transaction Utility value
• O(ip,tq)
– External Utility
• S(ip)
– Utilty value
• O(ip,tq)*s(ip)
– u(X, Tq), utility of an itemset X in transaction T
– Utility mining is to find all the high utility itemsets. An itemset X is a high utility
itemset if u(X) ≥ ε, where X ⊆ I and ε is the minimum utility threshold, otherwise,
it is a low utility itemset.

6. Literature survey
Author Title Description Drawback
Ying Liu Wei-keng
Liao Alok
Choudhary(2011)
A Fast High Utility
Itemsets Mining
Algorithm
Utility based data
mining is a new
research area
entranced in all
types of utility
factors like profit,
significance,
subjective
interestingness,
Aesthetic value
etc.,
Multiple scans, it
does not handle
the duplicate
itemsets

7. Literature survey
Author Title Description Drawback
Chowdhury Farhan
Ahmed, Syed
Khairuzzaman
Tanbeer,
Byeong-Soo Jeong,
and Young-Koo Lee
An Efficient
Candidate Pruning
Technique for
High Utility Pattern
Mining
Phase I may
overestimate some
itemsets due
to the different
definitions, only
one extra database
scan is needed
in Phase II to filter
out the
overestimated
itemsets. Our
algorithm
requires fewer
database scans, less
memory space and
less
computational cost.
This
algorithm suffers
from the same
problem of the
level-wise
candidate
generation and
test methodology

8. Literature survey
Author Title Description Drawback
Agrawal R
Srikant R.
Fast Algorithms for
Mining Association
Rules
Apriori uses
breadth-first search
and a hash tree
structure to count
candidate item sets
efficiently.
Assumes
transaction
database is
memory
resident.
Requires many
database scans

9. Proposed System
• FUM algorithm is used for mining all high utility itemsets.FUM algorithm
which generates high utility itemsets using CombinationGenerator.
• It is simpler and executes faster than Umining algorithm, when large
number of itemsets are identified as high utility itemsets and as the number
of distinct items increases in the input database.
• The CombinationGenerator(T) is a method which is used to generate all the
combinations of the items. It takes the Itemld and the level as the input
which is generally denoted by the variable loop.
• The factorial computation method is defined in this, to generate the factorial
of a given number.

10. Proposed system
• The combination generation is based on the concept proposed by
Kenneth H. Rosen, Discrete Mathematics and its applications .
• First the items for which the combination is to be generated is put in
the form of an array.
• Then the getNext() method is called until there are no more
combinations left. The getNext() method returns an array of integers,
which tells the order in which to arrange the original array of letters.
• Using HUHF itemsets generated by FUFM algorithm and HUI
generated using FUM algorithm and also generated three new
itemsets namely HULF,LUHF,LULF.

11. Algorithm
Step1:
compute the utility values of all single itemsets.
Step 2:
Begin a loop for processing each and every transaction present in the
DB one by one.
Step 3
The algorithm generates the itemsets in the current transaction.
Step 4
Whether a transaction defined by an itemset purchased in it, repeats
its occurrence in a later transaction.
If a later transaction also contains the same itemset purchased in
any of the previous transactions, then that transaction is ignored from
processing. In this way, the duplicate itemsets are removed.

12. Algorithm
• Step 5
The candidate itemsets are generated using the
CombinationGenerator(T) function, which takes itemset, purchased in a
particular transaction as input and generate the various possible
combinations of the itemset.
• Step 6
The items for which the combination is to be generated is put in the
form of an array. Then the getNext() method is called until there are no
more combinations left. The getNext() method returns an array of
integers, which tells the order in which to arrange the original array of
letters.
• Step 7:
The algorithm computes the utility value of each and every
candidate, U(C, T).

13. Algorithm
• Step 8
If the utility value of a candidate is found to be more than the
minimum utility threshold, which is given as input by the user, (say a sales
manager) then that particular candidate is added to the set of High Utility
Itemsets {H}
• Step 9
The condition C € H in simply ensures no duplicate high utility
itemsets are generated.

14. FUFM Algorithm
Step 1
Assign L = 1
Step 2
find the set of candidates of length L with support >= minSup
Step 3
compute exteded support for all candidates and output
utilityfrequent itemsets
Step 4
Assign L += 1
Step 5
use the frequent itemset mining algorithm to obtain new set of
frequent candidates of length L from the old set of frequent candidates
Step 6
stop if the new set is empty otherwise go to [3]

15. Architectural design

16. Modules
• New registration module all users must first get authorization before using
the shopping website. Users new to the web mart must register themselves
to shop online. User details like name, login id, password, email-ld, address
and other details are collected and their details are recorded in database for
authentication
• Shopping cart module used in web mart website to assist people making
purchases online. The cart module allows online shopping customers to
place items in the cart. Upon checkout, the website typically calculates a
total for the order as applicable. Customers can purchase multiple items
and pay for the item as one single order. This will save time that they would
spend paying for each item. Whenever a user is interested to buy a product
they can add it to cart by using add cart button

17. Modules
• Checkout module helps the customers to place their orders when
they checkout and to determine whether the transaction is genuine
or not
• Products so far maintained in the cart are displayed. For each
product quantity, total price are calculated and also no of items they
have ordered, net quantity, net amount are also calculated
• Administrative module is meant for administrative purpose. It aids
the web site manager/administrator to track customer orders, and to
take reports based on customers, products and customer
transactions.

18. Modules
• Item analysis module is used to calculate the High Utility Itemset
and High Utility High Frequent itemsets efficiently handles the
duplicate itemsets

19. Implementation

20. Screen shots
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21. New registration

22. Screen shot of add cart
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23. Shopping module

24. Checkout module

25. Administrative Module

26. Results
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High Utility Itemset

27. List of publications
• [1] V.Divya Devi, A.Salman Ayaz, “ITEMSET BASED ON CUSTOMER INTEREST
TO IMPROVE THE CRM BUSINESS”, National Conference on Advanced Computing
Technology, pp 15-21, Chennai, India, NACACT’13, May 2, 2013.
• [2] V. Divya Devi, A. Salman Ayaz,, “ITEMSET BASED ON CUSTOMER
INTEREST TO IMPROVE THE CRM BUSINESS”, National Conference on Advanced
Computing Technologies, pp 178-182 Chennai, India, NARACT’13, April 10, 2013.

28. References
• Agrawal R, Srikant R, ‘Fast algorithms for mining association rules’,
Proceedings of 20th InternationaConference on Very Large
Databases, Santiago, Chile, pp487–499, 1994.
• [2] Yao H, Hamilton H J, Butz C J, ‘A foundational approach to
mining itemset utilities from databases’, Proceedings of theThird
SIAM International Conference on Data Mining,Orlando, Florida, pp.
482–486, 2011
• [3] Chowdhury Farhan Ahmed, Syed Khairuzzaman Tanbeer,
Byeong-Soo Jeong, and Young-Koo Lee ‘An Efficient Candidate
Pruning Technique for High Utility Pattern Mining’. T.
Theeramunkong et al. (Eds.): PAKDD 2009, LNAI 5476, pp. 749–756,
2011.

29. THANK YOU
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