Download A comparative study of Frequent pattern mining Algorithms: Apriori

International Journal of Innovations & Advancement in Computer Science
IJIACS
ISSN 2347 – 8616
Volume 4, Special Issue
March 2015
A comparative study of Frequent pattern mining Algorithms:
Apriori and FP Growth on Apache Hadoop
Ahilandeeswari.G.1
Dr. R. Manicka Chezian2
Research Scholar,
Department of Computer Science,
NGM College, Pollachi, India,
Associate Professor,
Department of Computer Science,
NGM College, Pollachi, India,
1
2
Abstract— In Data Mining Research, Frequent pattern (itemset) mining plays an important role in
association rule mining. The Apriori and FP-growth algorithms are the most famous algorithms
which can be used for Frequent Pattern mining. The analysis of literature survey would give the
information about what has been done previously in the same area, what is the current trend and what
are the other related areas. This paper explains the concepts of Frequent Pattern Mining and two
important approaches candidate generation approach and without candidate generation. The paper
describes methods for frequent item set mining and various improvements in the classical algorithms
―Apriori‖ and ―FP growth‖ for frequent item set generation. Apache Hadoop is a major innovation in
the IT market place last decade. From humble beginnings Apache Hadoop has become a world-wide
adoption in data centers. It brings parallel processing in hands of average programmer. This paper
presents a literature review of frequent pattern mining algorithms on Hadoop .
Keywords : Data mining, Association rule, Frequent itemset mining ,Apriori and FP growth
algorithm.
where it is needed to discover useful patterns
1. INTRODUCTION
Frequent pattern mining [1] plays a major field
in customer’s transaction database. A
in research since it is a part of data mining.
customer’s transaction database is a sequence
Many research papers, articles are published in
of transactions (T=t1…tn), where each
the field of Frequent Pattern Mining (FPM).
transaction is an itemset (ti ⊆I). An itemset
This chapter details about frequent pattern
with k elements is called a k-itemset. An
mining algorithm, types and extensions of
itemset is frequent if its support is greater than
frequent pattern mining, association rule
a support threshold, denoted by min supp. The
mining algorithm, rule generation, suitable
frequent itemset problem is to find all frequent
measures for rule generation. Frequent pattern
itemset in a given transaction database. The
mining is fundamental in data mining. The
first and most important solution for finding
goal is to compute on huge data efficiently.
frequent itemsets, is the Apriori algorithm.
Finding frequent patterns plays a fundamental
The fundamental frequent pattern algorithms
role in association rule mining, classification,
are classified into two ways as follows:
clustering, and other data mining tasks.
1.Candidate generation approach (E.g. Apriori
Frequent pattern mining was first proposed by
algorithm, AprioriTID, Apriori Hybrid)
Agarwal et. al. [1] for market basket analysis
in the form of association rule mining.
2.Without candidate generation approach (E.g.
Frequent Itemset mining came into existence
FP Growth algorithm)
451 Ahilandeeswari.G., Dr. R. Manicka Chezian
International Journal of Innovations & Advancement in Computer Science
IJIACS
ISSN 2347 – 8616
Volume 4, Special Issue
March 2015
Get frequent items
Understanding the FP Tree Structure:
The frequent-pattern tree (FP-tree) is a
compact structure that stores quantitative
information about frequent patterns in a
database. One root labeled as ―null‖ with a set
of item-prefix subtrees as children, and a
frequent-item-header table.[2].
Generate Candidate Itemset
Get frequent itemset
2. BASIC APPROACH’S OF FREQUENT
PATTERN MINING
2.1 Candidate Generation Approach
Apriori: Apriori proposed by R. Rakesh[1] is
the fundamental algorithm. It searches for
frequent itemset browsing the lattice of
itemsets in breadth. The database is scanned at
each level of lattice. Additionally, Apriori uses
a pruning technique based on the properties of
the itemsets, which are: If an itemset is
frequent, all its sub-sets are frequent and not
need to be considered.
AprioriTID: AprioriTID algorithm uses the
generation function in order to determine the
candidate item sets. The only difference
between the two algorithms is that, in
AprioriTID algorithm the database is not
referred for counting support after the first
pass itself.
Apriori Hybrid: Apriori Hybrid uses Apriori
in the initial passes and switches to AprioriTid
when it expects that the candidate item sets at
the end of the pass will be in memory[3].
2.1.1 Implementation
Algorithm
of
Generate
Set-null
yes
Generate strong rules
Fig 1: High level design of Apriori
Step
1
Step
2
Step
3
Apriori
Start
452
No
Ahilandeeswari.G., Dr. R. Manicka Chezian
Find all frequent itemsets:
Get frequent items:
Items whose occurrence in database is
greater than or equal to the
min.support threshold.
Get frequent itemsets:
Generate candidates from frequent
items.
Prune the results to find the frequent
itemsets.
Generate strong association rules
from frequent itemsets:
Rules which satisfy the min.support
and min.confidence threshold.
Fig 2: Apriori Algorithm
International Journal of Innovations & Advancement in Computer Science
IJIACS
ISSN 2347 – 8616
Volume 4, Special Issue
March 2015
2.2
Without
Candidate
Generation
Approach
FP-growth: The principle of FP-growth
method [5] is to found that few lately frequent
pattern mining methods being effectual and
scalable for mining long and short frequent
patterns. FP-tree is proposed as a compact data
structure that represents the data set in tree
form.
2.2.1 Implementation of FP Growth
Algorithm
Calculate the support count of
Each items
Sort items in decreasing Counts
Read transaction t
Fig 3: Activity design of FP growth
First a transaction t is read from database. The
algorithm checks whether the prefix of t maps
to a path in the FP tree. If this is the case
support count of the corresponding node in the
tree are incremented. If there is no
overlapped path, new nodes are created with
the support count 1.
The dataset is scanned to determine
the support of each items. The
infrequent items are discarded in FP
tree. All Frequent items are ordered
Step 2 based on their support
The algorithm does the second pass
over the data and construct the FP
tree
Fig 4: Algorithm for FP Growth
Step 1
2.3.
Comparative analysis
The two algorithms discussed above are
widely studied algorithms for frequent pattern
Increment the
mining. The apriori algorithm works by
frequency
Non
generating candidate itemsets while the FPcount for each
Overlapped
growth algorithm works without generating
overlapped
Prefix found
the candidate sets.
items
Overlapped
The apriori algorithm has the following
Prefix found
bottlenecks:
1.) Difficult to handle huge number of
Create a new node
candidate itemsets. The candidate generation
labeled with the items
can be very costly with the increasing size of
in t
database.
2.) It is tedious to repeatedly scan the huge
Set the frequency
Create new
databases.
count to 1
node for
The FP-growth algorithm is quite a different
none
algorithm from its predecessors. It works by
overlapped
generating a prefix-tree data structure known
items
as FP-tree from two scans of the database.
This algorithm doesn’t need to scan the
database multiple times. The main drawbacks
return
of the apriori algorithm are removed with the
introduction of the FPgrowth algorithm. The
453 Ahilandeeswari.G., Dr. R. Manicka Chezian
Has next
International Journal of Innovations & Advancement in Computer Science
IJIACS
ISSN 2347 – 8616
Volume 4, Special Issue
March 2015
table represents the comparison of two
algorithms studied here based on different
parameters.[4]
Table 1: Differentiation between Apriori and
FP Growth
Parameter
Apriori
FP Growth
Technique
Memory
utilization
No of scans
Time
2.4.
Use Apriori
property
and join and
prune
property
Constructs
FP-tree and
conditional
pattern base
satisfying
minimum
support.
Large
Lesser
memory
memory due
space
for to compact
candidate
structure
itemsets
Multiple
Scans
the
scans
of database
database.
twice only
Execution
Execution
time is large time
is
because of smaller.
candidate
itemsets
generation
Apache Hadoop
The Apache™ Hadoop® develops opensource software for reliable, scalable,
distributed computing [6]. The Apache
Hadoop software library is a framework that
allows for the distributed processing of large
data sets across clusters of computers using
simple programming models. Hadoop is a,
Java-based programming framework that
supports the processing of large data sets in a
distributed computing environment and is part
454
of the Apache project sponsored by the
Apache Software Foundation. Hadoop was
originally conceived on the basis of Google's
Map Reduce, in which an application is
broken down into numerous small parts [9]. It
is designed to scale up from single servers to
thousands of machines, each offering local
computation and storage. Rather than rely on
hardware to deliver high-availability, the
library itself is designed to detect and handle
failures at the application layer, so delivering a
highly-available. service on top of a cluster of
computers, each of which may be prone to
failures[15]. Hadoop framework is popular for
HDFS and Map Reduce. The Hadoop
Ecosystem also contains different projects
which are discussed below [7] [8]: The
Hadoop includes these modules:

Hadoop Common: The common
utilities that support the other Hadoop
modules. It contains libraries and utilities
needed by other Hadoop modules.

Hadoop Distributed File System
(HDFS™): A distributed file system that
provides high-throughput access to application
data. HDFS is the Hadoop file system and
comprises two major components: namespaces
and blocks storage service. The namespace
service manages operations on files and
directories, such as creating and modifying
files and directories. The block storage service
implements data node cluster management,
block operations and replication.

Hadoop YARN: A framework for job
scheduling and cluster resource management.
YARN is a resource manager that was created
by separating the processing engine and
resource management capabilities of Map
Reduce as it was implemented in Hadoop 1.
YARN is often called the operating system of
Ahilandeeswari.G., Dr. R. Manicka Chezian
International Journal of Innovations & Advancement in Computer Science
IJIACS
ISSN 2347 – 8616
Volume 4, Special Issue
March 2015
Hadoop because it is responsible for managing
and monitoring workloads, maintaining a
multi-tenant
environment, implementing
security controls, and managing high
availability features of Hadoop.
Apriori
Khurana, TID:
K., and Used for
smaller
Sharma
problems

Hadoop Map Reduce: A YARNbased system for parallel processing of large
data sets. The Hadoop Map Reduce
framework consists of one Master node
termed as Job Tracker and many Worker
nodes called as Task Trackers.
3.
REVIEW
ON
SEVERAL
IMPROVEMENTS OF
APRIORI
AND
FP
GROWTH
ALGORITHM ON APACHE HADOOP
The table clearly summarizes the essential
information of all the algorithms discussed in
the paper. The main purpose of the table is to
highlight the application of all the above stated
algorithms. In this section we are going to
discuss content of the paper, proposed system,
research gape and observed parameters of
different papers.
Hunyadi,
D.
Borgelt,
C.
Table 2: Represents a Comparative study of
Algorithms
Author
Khurana,
K., and
Sharma
Borgelt,
C.
455
Technique
Apriori
Hybrid:
Used
where
Apriori
and
AprioriTID
used.
Apriori:
Best
for
closed item
sets.
Benefit
Better than both
Apriori
and
AprioriTID.[3]
FP
Growth:
Used
in
cases
of
large
problems
as
it
doesn’t
require
generation
of
candidate
sets.
candidate sets
from only those
items that were
found
large.
[10]
1. Doesn’t use
whole database
to
count
candidate sets.
2. Better than
SETM.
3. Better than
Apriori
for
small databases.
4.
Time
saving.[3].
1.Only 2 passes
of
dataset,,
Compresses
data set.
2. No candidate
set generation
required
so
better
than
éclat,
Apriori.[11][10]
In table 3, Different algorithm’s proposed
system and their limitations are discussed
which give us a research gape in that papers.
Table 3: Comparative Analysis of Apriori
and FP Growth Algorithm on Apache
Hadoop
1. Fast
2.
Less
candidate sets,
3.
Generates
Ahilandeeswari.G., Dr. R. Manicka Chezian
International Journal of Innovations & Advancement in Computer Science
IJIACS
ISSN 2347 – 8616
Volume 4, Special Issue
March 2015
Author
Othman
Yahya,
Osman
Hegazy,
Ehab
Ezat.
Pallavi
Roy.
456
Content and
Proposed
System
Authors
implemented
an efficient
MapReduce
Apriori
algorithm
(MRApriori)
based
on
HadoopMapReduce
model which
needs
only
two
phases
(MapReduce
Jobs) to find
all frequent
kitemsets,
In this thesis
association
rule mining
was
implemented
on
hadoop. An
association
rule mining
helps
in
finding
relation
between the
items or item
sets in the
given data.
[13]
using
hashing.
Research
Gape
In this paper
author
implement
Apriori
algorithm on
a single
machine or
can say a
stand-alone
mode
so
there
are
some chance
to
implement
on multiple
node.[12]
Limitation of
this
algorithm is
that
it
can
generated too
many
association
rules
and
also small
dataset may
not
give
good
performance.
There
are
some
chance
to
make
this
algorithm
faster
by
Sandy
Moens,
Emin
Aksehirli,
and Bart
Goethals.
In this paper,
there are two
new methods
introduce for
FIM: DistEclat focuses
on
speed
while
BigFIM is
optimized to
run on
really large
datasets [14].
In this paper
one issues is
that,
Dist-Eclate
or
original
Eclat
algorithms
use vertical
datasets, so
datasets has
to
be
converted
from
horizontal to
vertical data.
In table 4, different parameters are observed in
time of performance evolutions which
summarize in this table.
Table 4: Observed Parameters
Pape Executio Efficienc Load
r
n time
y
Balancin
g
12
yes
No
No
13
yes
Yes
No
14
yes
Yes
No
Ahilandeeswari.G., Dr. R. Manicka Chezian
International Journal of Innovations & Advancement in Computer Science
IJIACS
ISSN 2347 – 8616
Volume 4, Special Issue
March 2015
4. CONCLUSION
In recent years the size of database has
increased rapidly. Therefore require a system
to handle such huge amount of data. In this
paper, algorithmic aspects of association rule
mining are dealt with. From a broad variety of
efficient algorithms the most important ones
are compared. The algorithms are systemized
and their performance is analyzed based on
runtime and theoretical considerations.
Despite the identified fundamental differences
concerning employed strategies, runtime
shown by algorithms is almost similar. The
comparison table shows that the Apriori
algorithm outperforms other algorithms in
cases of closed item sets whereas FP growth
displayed better performance in all the cases.
The overall goal of the frequent item set
mining process helps to form the association
rules for further use. This paper gives a brief
survey on frequent pattern mining algorithm
Apriori and FP Growth algorithm on Apache
Hadoop.
4.
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Algorithms for Mining Association Rules.‖,
International Conference on very large
Databases, proc.20th , pp 487-499, june 1994.
[2] Prashasti Kanikar, Twinkle Puri, Binita
Shah, Ishaan Bazaz, Binita Parekh," A
Comparison of FP tree and Apriori algorithm.
",International Journal of Engineering
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6 , pp 78-82, June 2014.
[4] Sumit Aggarwal and Vinay Singal, "A
Survey on Frequent pattern mining
Algorithms.",
International Journal of
Engineering Research & Technology (IJERT),
ISSN: 2278-0181, Vol. 3 Issue 4, pp 26062608, April 2014.
[5] J. Wang, J. Han, and J. Pei, ― Searching for
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(KDD'03), Proc. 2003, ACM SIGKDD Aug.
2003.
[6] Ms. Dhamdhere Jyoti L., Prof. Deshpande
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[7] Ferenc Kovacs and Janos Illes ―Frequent
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International conference on Computational
Cybrnetics, Volume 2 Issue 4, june 2013.
[8] Ms. Dhamdhere Jyoti L., Prof. Deshpande
Kiran B. "A Novel Methodology of Frequent
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[3] Khurana K and Sharma.S, ―A comparative
analysis of association rule mining
algorithms.‖,
International
Journal
of
[10] Borgelt, C. “Efficient Implementations of
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Apriori and Eclat‖. Workshop of frequent item
3, Issue 5, pp 38-45, May 2013.
457 Ahilandeeswari.G., Dr. R. Manicka Chezian
International Journal of Innovations & Advancement in Computer Science
IJIACS
ISSN 2347 – 8616
Volume 4, Special Issue
March 2015
set mining implementations (FIMI 2003,
Melbourne, FL, USA).
[11] Hunyadi, D. ―Performance comparison of
Apriori and FP-Growth Algorithms in
Generating Association Rules‖. Proceedings
of the European Computing Conference ISBN:
978-960-474-297-4.
[12] Othman Yahya, Osman Hegazy, Ehab
Ezat. ―An Efficient Implementation of Apriori
Algorithm Based On Hadoop-Mapreduce
Model‖. IJRIC, pp. 59:67, june 2012
458
[13] Pallavi Roy. ―Mining Association Rules
in Cloud‖, M.S thesis, Dept. Computer. Eng,
North Dakota State University of Agriculture
and Applied Science, Fargo, North Dakota,
August 2012.
[14] Sandy Moens, Emin Aksehirli, and Bart
Goethals. ―Frequent itemset mining for big
data‖.IEEE International Conference on Big
Data, pp. 111–118, May 2013
[15] Sivaraman .E,
Manickachezian .R,
―High Performance and Fault Tolerant
Distributed File System for Big Data Storage
and Processing Using Hadoop‖, IEEE Xplore,
ISBN: 978-1-4799-3967-1
Ahilandeeswari.G., Dr. R. Manicka Chezian