International Journal of Engineering Trends and Technology (IJETT) – Volume 31 Number 2- January 2016
An Efficient Frequent Pattern Generation with Utility and Flag
Matrix Model
Mutyala Narendra1, Boddu Nanda Kishore2
1,2
Final M.Tech Student1, Assistant professor 2
Dept of CSE, Avanthi Institute of Engineering and Technology, Tamaram, Makavarapalem, Visakhapatnam,
Andhrapradesh, India.
Abstract:
Extraction of association rules from set of
patterns is always an interesting research issue in the
field of knowledge and data engineering. In this
paper we are proposing an efficient comparative
analysis between FP growth, utility based pattern
generation approach and flag matrix based pattern
generation . In this approach we will reduce the
number of times of database scans and the space and
time complexity to generate frequent item sets. The
flag matrix approach is more efficient than the FP
growth and utility pattern matching techniques.
Every pattern mining algorithm generates same
patterns but time complexity and optimality of
patterns is different.
I.INTRODUCTION
Frequent pattern mining consists of
developing data mining algorithms to discover
interesting and unexpected and useful patterns in
databases. Frequent pattern mining algorithms are
applied on different kind of data such as transactional
databases, graphs, streams, spatial data etc. Frequent
pattern mining algorithms are designed to find the
different kind of patterns like sub graphs, sequence
patterns, rules, lattices etc. There are following
examples for patter mining:
The most popular algorithm for pattern mining is
Apriori algorithm. It is mainly designed for applying
on transactional databases to get the pattern in
transactions. A transaction is defined as set of
different items. Apriori takes two inputs. One is
minsup threshold which is set by user and other is
transactional database which
consists of transactions. Apriori gives output as
frequent Itemsets [1]
We are proposing an association rule based approach
to find association between entities or URL to
identify the frequent patterns for an input query.
Various approaches available for pattern based
techniques like Apriori, Utility, Apriori TD, FP
growth etc. Apriori is one of the simple frequent
pattern generation algorithm but main drawbacks
of this approach is multiple dataset scan and
candidate set of each and every frequent item set,
increases the time complexity when item set or set of
URLs are more, so FP growth algorithm is one of the
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efficient algorithm to find frequently visited URLs by
constructing FP tree and finds frequent patterns from
FP tree
The sequence database is the combination of
sequences. The sequential rule has the form XY
where X and Y are two distinct non empty sets of
items. The meaning of this rule is the item of X
follows by Y in order and vice versa. The goal of
sequential rule mining is to discover all sequential
rules which is having thresholds given by the user
named as “minsup” and “minsconf”. This is called
Rule Growth Algorithm.
Association Rule Mining is the procedure to
find frequent patterns, correlations and associations
from data sets that are found in different databases
like relational databases, transactional databases and
other form of data repositories. The main applications
of association rule mining are as follows:
Basket Data Mining:
It is used to analyze the association of
purchased items in a basket
Cross Marketing:
It is used to work with other businesses and
complement of your own.
Catalog Design:
It is used for the selection of items in
business of catalog are designed to complement each
other.
II.RELATED WORK
Even though various traditional approaches
available for generation of frequent patterns and
association rules, they are not optimal in terms of
time complexity due to approaches of candidate set
generation, multiple database scans ,two time scan
and other issues. Only few approaches can identify
the internal and optimal patterns from the frequent
patterns apart from the regular frequent patterns. The
main disadvantages of the traditional approaches are,
candidate set generation is difficult if the size of the
database is huge and Multiple database scans are
need to generate frequent items sets
An association rule is an implication or if-then-rule
which is supported by data. The motivation for the
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International Journal of Engineering Trends and Technology (IJETT) – Volume 31 Number 2- January 2016
development of association rules is market basket
analysis which deals with the contents of point-ofsale
transactions of large retailers. A typical association
rule resulting from such a study could be \90 percent
of all customers who buy bread and butter also buy
milk". Insights into customer behaviour may also be
obtained through customer surveys, but the analysis
of the transactional data has the advantage of being
much cheaper and covering all current customers.
Compared to customer surveys, the analysis of
transactional data does have some severe limitations,
however. For example, point-of-sale data typically
does not contain any information about personal
interests, age and occupation of customers.
Nonetheless, market basket analysis can provide new
insights into customer behaviour and has led to
higher pro_ts through better customer relations,
customer retention, better product placements,
product development and fraud detection.
Itemsets and Associations:
In this section a formal mathematical model is
derived to describe itemsets and associations to
provide a framework for the discussion of the apriori
algorithm. In order to apply ideas from market basket
analysis to other areas one needs a general
description of market baskets which can equally
describe collections of medical services received by a
patient during an episode of care, subsequences of
amino acid sequences of a protein, and collections or
words or concepts used on web pages. In this general
description the items are numbered and a market
basket is represented by an indicator vector.
Utility mining is defined as the identification of
Itemsets with high utility. Utility can be measured as
cost, price and other expressions with which user
needs The objective of Utility mining is to find utility
Itemsets having high or greater or equal to minimum
utility thresholds.[3]
Boolean matrix is an integer matrix in which each
element is 0 and 1. It is also called logical matrix.
The number of m*n binary matrices is 2 mn. .
Examples of Boolean matrix are incidence matrix,
permutation matrix, bi-adjacency matrix. The game
rules can be checked by using Boolean matrix.
Modular arithmetic operation can be performed in
binary matrices. The matrix representation of equality
relation is identity matrix.
III.PROPOSED WORK
We propose an efficient association rule mining
approach with flag matrix representation with one
time database scan. Algorithm need not scan the
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database multiple times and no candidate set
generations. It reduces the space and time complexity
for generation of frequent patterns and association
rules, for comparative analysis we generated rules
through fp growth algorithm, utility and flag matrix
representation and in the second phase frequent
patterns can be forwarded to genetic algorithm for
optimal pattern generation.
Difficulty and time complexity in the candidate set
generation was reduced by generating the flag matrix
for the database and multiple database scans are
reduced to one time scan of database
FP growth algorithm is a two phase
approach for generation of frequent patterns in first
phase it constructs the fp tree and second phase it
constructs the conditional tree for frequent pattern
generation. In phase one ,algorithm reads one
transaction at a time and create a branch wet
sequence of nodes and edges, before creation of edge
it checks for a path is any previously available with
first element of the pattern, if it is found then
increment the counter of the item and create one
more branch from that node, we continue the process
until the last transaction.
Conditional pattern tree can be generated for each
individual element and all possible combinations,
traverse from the suffix nodes and gets all possible
combinations and count with this start node and if it
meets the minimum threshold then it can be taken as
frequent item, otherwise it can be ignored.
Utility Pattern Mining:
Most of the frequent pattern mechanisms works on
frequency of the item but not on the importance or
utility of the item, utility growth model works based
on utility of the item set.An item utility can be
computed with product of item quantity and product
and then integration of these products for all
transactions can be taken as total utility ,if it meets
the mininmum threshold value it can be taken as
utility item.
For finding the utility of n item set we consider the
frequent item set,computes sub item utilities
individual with respect to one transction and
continues the process until it reaches the final
transaction then only satisfied patterns can be
considered.
Let I={i1, i2, i3, . . . , in} be a set of items and DB be
a database composed of a utility table and a
transaction table. Each item in Ihas a utility value in
the utility table. Each transaction T in the transaction
table has a unique identifier (tid) and is a subset of I,
in which each item is associated with a count value.
An itemset is a subset of I and is called
ak-itemset if it contains k items.
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International Journal of Engineering Trends and Technology (IJETT) – Volume 31 Number 2- January 2016
Definition 1.The external utility of item i, denoted as
eu(i), is the utility value of i in the utility table of DB.
Definition 2.The internal utility of item i in
transaction T, denoted as iu(i, T), is the count value
associated with i in T in the transaction table of DB.
Definition 3.The utility of item i in transaction T,
denoted as u(i, T), is the product of iu(i, T) and eu(i),
where u(i, T) = iu(i, T) × eu(i).
eu(e) = 4, iu(e, T5) = 2, and u(e,T5)= iu(e, T5) ×
eu(e) = 2 × 4 = 8.
Definition 4.The utility of itemset X in transaction T,
denoted as u(X, T), is the sum of the utilities of all
the items in X Σ in T in which X is contained, where
u(X, T) = I ∈ X∧ X⊆T u(i; T).
Definition 5.The utility of itemset X, denoted as
u(X), is the sum of the utilities of X in all the
transactions containing X in DB, where u(X) =
Σ T∈ DB∧ X⊆T u(X; T).
Flag Matrix:
Flag matrix is a novel technique for generation of
frequent patterns, it reduces the traditional
complexity issues like Candidate set generation and
multiple data base scans by constructing a simple
matrix between transactions and items or data objects
here frequent items can be generated based on flag
values if any item exists specific transaction then it
can be set to 1 else 0.
Algorithm for Flag Matrix :
1: While (Patterns available)
2: Load the individual patterns Pifrom transaction
table
3: Generate a matrix with l rows and m columns
Where „l‟ is item in transaction and „m „ is id of
the transaction
4: if corresponding item „l‟ isavailableinspecific
transaction „m‟ then
Set intersection (l,m)=‟1‟
else set to‟ 0‟.
5: Continuesteps 2 to 5
completed
until all transactions
Now we can extract frequent patterns from the matrix,
to extract frequent 1 itemset, initially count number
of ones in vertical columns with respect to item, if it
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matches minimum threshold values then treat it as
frequent item else ignore, continue same process for
2 itemset,check whether two items have „1‟ in their
corresponding vertical columns then increment,
continue until all transactions verified. If total count
greater than threshold value then treat it as frequent
item
1: Load item_set {I1,I2…In) and Initialize the
count:=0 and final_counter :0
2: for i:=0 ;i< n ;i++
For j:=0 j<trans _size() ;j++
If intersection of (i,j)==1 then
Count :=+1;
Next
If counter ==Ii .size_() then add items to list
Next
3: Set minimum support count value (t)
4: for k=0;k<item_list_size ;k++
Ifitem_list[k].count >= t Then
add to list of frequent items
Next
5: return frequent pattern list
Flag matrix can be generated based on the existence
of the item with respect to transactions . It initially
reads first transaction from the database ,for example
it contains “a,b,c,d” ,in corresponding positions of
matrix , item values can be
set to „1‟ in
corresponding transaction else „0‟ and consider
second transaction “a,c,e”,set the corresponding item
positions to „1‟ in second transaction and continue
the process until all transactions placed in matrix
representation..
IV. CONCLUSION
We have been concluding our current research work
with efficient frequent models,fp growth approach
ignores the unnecessary overhead of candidate
generations and multiple data base scans, but it is not
suitable when data is large because traversing
problem and complex while tree construction, utility
based approach gives the utility of the item and items
sets apart from frequency but it needs few scans. Flag
matrix makes one time databases scan for generation
of matrix, so need to scan the databases for frequent
item sets because we can generate from matrix.
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International Journal of Engineering Trends and Technology (IJETT) – Volume 31 Number 2- January 2016
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BIOGRAPHIES
MutyalaNarendrapursuingm.tech
in
avanthiinst of engg& tech, tamaram,
makavarapalem,
visakhapatnam,
andhrapradesh, india. He received
mca(master of computer applications)
from jntuk (dadi inst. of engg& tech,
anakapalli, visakhapatnam) in 2010. His
interested areas are cloud computing, network security,
data warehousing.
Boddu Nanda Kishore working as
assistant professor in the dept of
computer science and engineering,
avanthi institute of engineering and
technology (affiliated to jntuk),
tamaram,
makavarapalem,
visakhapatnam, andhrapradesh, india.
He received his m.tech in computer
science and engineering from jntuk. his main areas of
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