17-03-2014, 05:34 PM
Abstract
Association Rule Mining (ARM) is the most
essential technique for data mining that mines hidden
associations between data in large databases. The
most important function of ARM is to find frequent
itemsets. Frequent closed itemsets (FCI) is an
important condense representation method for frequent
itemsets, and because of its importance in recent years,
there have been many algorithms implemented for it.
One of the most fundamental algorithms for frequent
closed itemset is A-close. In this paper, we optimize
this algorithm using both optimized techniques
"reducing pruning time" and "reducing database size",
called "A-close+".. Results show that the performance
cost of our algorithm is considerably less than A-close.
1. Introduction
Association rule mining (ARM) is one of the most
important data mining techniques. ARM aims at
extraction, hidden relation, and interesting associations
between the existing items in a transactional database.
It is highly useful in market basket analysis for stores
and business centers. For example, database mining of
a department store customers reveal that those who buy
milk would buy butter in 60% of occasions, and such
principle is observed in 80% of transactions. In this
example, the above-mentioned probability is called
confidence percentage, and a percentage of
transactions which cover this rule is termed support
percentage. To find the rules, user should set a
minimum amount for support and confidence which
are called minimum support (min-sup) and minimum
confidence (min-conf) respectively [1].
The main step in association rule mining is the
mining frequent itemsets. In effect, with frequent
itemsets in hand, generating association rules would be
highly straightforward. Frequent itemsets mining often
generates a very large number of frequent itemsets and
rules. As such, it reduces the efficiency and power of
mining. To overcome the problem, in recent years
condensed representation has been used for frequent
itemsets [3, 7]. A popular condensed representation
method is using to frequent closed itemsets. Compared
with frequent itemsets, the frequent closed itemsets is a
much more limited set but with similar power. In
addition, it decreases redundant rules and increases
mining efficiency. Many algorithms have been
presented for mining frequent closed itemsets, and
A-close proved to be a fundamental one [6]. In this
article, we have used two optimal techniques, i.e.
"reducing pruning time" and "reducing database size",
to arrive at our developed algorithm A-close+.
The paper is structured as follows; Section 2
introduces frequent closed itemsets and related
concepts. A-close algorithm and A-close+ are
presented in Sections 3 and 4 respectively. Section 5
briefs on research results and conclusions are given in
section 6.
Association Rule Mining (ARM) is the most
essential technique for data mining that mines hidden
associations between data in large databases. The
most important function of ARM is to find frequent
itemsets. Frequent closed itemsets (FCI) is an
important condense representation method for frequent
itemsets, and because of its importance in recent years,
there have been many algorithms implemented for it.
One of the most fundamental algorithms for frequent
closed itemset is A-close. In this paper, we optimize
this algorithm using both optimized techniques
"reducing pruning time" and "reducing database size",
called "A-close+".. Results show that the performance
cost of our algorithm is considerably less than A-close.
1. Introduction
Association rule mining (ARM) is one of the most
important data mining techniques. ARM aims at
extraction, hidden relation, and interesting associations
between the existing items in a transactional database.
It is highly useful in market basket analysis for stores
and business centers. For example, database mining of
a department store customers reveal that those who buy
milk would buy butter in 60% of occasions, and such
principle is observed in 80% of transactions. In this
example, the above-mentioned probability is called
confidence percentage, and a percentage of
transactions which cover this rule is termed support
percentage. To find the rules, user should set a
minimum amount for support and confidence which
are called minimum support (min-sup) and minimum
confidence (min-conf) respectively [1].
The main step in association rule mining is the
mining frequent itemsets. In effect, with frequent
itemsets in hand, generating association rules would be
highly straightforward. Frequent itemsets mining often
generates a very large number of frequent itemsets and
rules. As such, it reduces the efficiency and power of
mining. To overcome the problem, in recent years
condensed representation has been used for frequent
itemsets [3, 7]. A popular condensed representation
method is using to frequent closed itemsets. Compared
with frequent itemsets, the frequent closed itemsets is a
much more limited set but with similar power. In
addition, it decreases redundant rules and increases
mining efficiency. Many algorithms have been
presented for mining frequent closed itemsets, and
A-close proved to be a fundamental one [6]. In this
article, we have used two optimal techniques, i.e.
"reducing pruning time" and "reducing database size",
to arrive at our developed algorithm A-close+.
The paper is structured as follows; Section 2
introduces frequent closed itemsets and related
concepts. A-close algorithm and A-close+ are
presented in Sections 3 and 4 respectively. Section 5
briefs on research results and conclusions are given in
section 6.