IRJET- Effecient Support Itemset Mining using Parallel Map Reducing

International Research Journal of Engineering and Technology (IRJET) e-ISSN: 2395-0056
Volume: 05 Issue: 04 | Apr-2018 www.irjet.net p-ISSN: 2395-0072
© 2018, IRJET | Impact Factor value: 6.171 | ISO 9001:2008 Certified Journal | Page 600
Effecient Support Itemset Mining using Parallel Map Reducing
Anuradha S1, Niharika A2, Manaswini G3, Manisha N4
1 Associate Professor, 2,3,4 Students, Dept. of Computer Science Engineering, Aurora’s Technological & Research
Institute, Hyderabad, India
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Abstract - Enormous data is being generated continuously
by many scientific applications such as bioinformatics and
networking. As each event is characterized by a wide variety
of features, high-dimensional data sets are being generated.
Different exploratory data mining algorithms are required to
discover the hidden correlations among data from these
complex data sets. Frequent itemset mining is an effective but
computationally expensive technique that is usually used to
support data exploration. Unfortunately, all the existing
algorithms are designed to cope with low dimensional data
only. This paper presents the performance analysis of three
data exploration algorithms based on frequent closed itemset
mining and association rule generation on a high dimensional
dataset and suggests a better alternative- the Parallel
MapReduce algorithm for itemset mining and rule generation
on high dimensional data. The experimentalresultsperformed
on high-dimensional dataset shows the efficiency of proposed
approach interms of execution time, load balancing, and
robustness.
Key Words: data exploration, high-dimensional data,
association rule mining, map reduce algorithm, weka
tool, Hadoop framework
1. INTRODUCTION
With the advent of “Big Data” and the increasing
capabilities of latest applications to generate and manage
huge volumesofdata,theimportanceofknowledgediscovery
and data analysis has changed dramatically [1]. This
increased the significance of data mining technology. The
need for efficient and highly scalable data mining tools has
increased with the size of datasets [2]. The explosion of Big
Data has increased the generation of high dimensional data.
For instance, data on health status of patients, this data is
characterized by 100+ measured/recordedparametersfrom
blood analysis, immune system status, genetic background,
nutrition etc. Performing itemset mining on such data has
been a challenging task.
Frequent itemset mining is most exploratory datamining
technique used to discover frequently occurring itemsets
according to user-specified threshold frequency and min-
support. While there are many algorithms that had been
developed for frequent pattern mining [3, 4, 5],
unfortunately, they are designed to cope with low
dimensional datasets. The curse ofdimensionality[6],incase
of high-dimensional data, renders most current algorithms
impractical. This work shows the performance analysis of
Apriori, Predictive Apriori and Filtered Associator
algorithms for frequent itemsetminingandassociation rules
generation on high dimensional datasets and proposes
mapreducing is an efficient solution to the problem.
This paper is organized as follows: association rule
mining literature study, discussion on association mining
algorithms, experimental results, and conclusion of results.
2. LITERATURE STUDY
2.1 Frequent Itemset mining
Frequent itemset mining is one of the most complex
exploratory techniques in data mining and provides the
ability to discover transactional databases [6]. Formally,
frequent itemset mining can be explained as follows:
Consider a set of items B = { i1, i2, i3 …. in }, known as an
item base, and a database of transactions denoted byT={ t1,
t2, t3 …. tm }. Each element in the item base denotes an item
and each element in the database set consists of a
combination of items. An itemset refers to any subset of
itembase B. Therefore, eachtransactioncanbeconsidered as
an itemset. Frequent itemset mining is determined by two
criteria - support and confidence. To explain these terms
consider two random items X and Y. Support measures the
percentage of transactions in T that contain both X and Y.
Support ( X → Y ) = P ( X ⋃ Y )
Confidence measures the percentage of transactions in T
containing X that also contain Y. In other words, the
probability of finding Y, given X is already present in the
transaction.
Confidence ( X ⟶ Y ) = P ( Y | X )
Confidence ( X → Y ) = Support (X ∪ Y ) | Support(X)
Support count is the frequency of occurrence of an
itemset. It is denoted by σ.
For frequentitemsetmining,theuserspecifiesaminimum
supportcount value isconsidered as the main parameter[7].
This value iscalled as threshold value. Anitemsetissaidtobe
frequent only if it satisfies the threshold value, that is, only if
the support count of the itemset is greater than or equal to
the threshold value. Themaingoaloffrequentitemsetmining
is to discover all the itemsets belonging to set B that appear
frequently in the transaction set T.
2.2 Association Rules
Frequent itemsets are used to generate association rules.
In general, association rules are used to analyse very large
binary or discretized datasets. One common application is to
discover hidden patterns within variables within
transactional databases, and this pattern is called ‘market-
basket analysis’. Association rules can be defined as a
statement of the form [7]:

X ⇒ Y
where X,Y⊂B such that X ≠ ø, Y ≠ ø and X ∩ Y = ø.
Association rules are often used to analyze sales
transactions[9]. For example, it might be noted that
customers who buy cereal atthe grocery storeoftenbuymilk
at the same time. In fact, association analysis might find that
85% of the checkout sessionsthat include cereal alsoinclude
milk. This relationship could be formulated as the following
rule.
Cereal implies milk with 85% confidence.
It is valuable for direct marketing, sales promotions, and for
discovering business trends.Market-basketanalysiscanalso
be used effectively forstorelayout, catalog design,andcross-
sell.Associationmodelinghasimportantapplicationsinother
domains as well. For example, in e-commerce applications,
association rules may be used for Web page personalization.
An association model might find that a user who visits pages
A and B is 70% likely to also visit page C in the same session.
Based on this rule, a dynamic link could be created for users
who are likely to be interested in page C. The associationrule
could be expressed as follows.
A and B imply C with 70% confidence
3. METHODOLOGY
This work is done with performance analysis of
association mining on a low dimensional data set (around17
attributes) using Apriori, Predictive Apriori and Filtered
Associatemining algorithms [10, 11, 12]and the resultswere
studied. In the next step same algorithms are applied with a
high-dimensional data set of nearly 70 attributes, and
observed the affect of curse of dimensionality. Then parallel
map reduce algorithm is applied on the hig-dimensionaldata
set to study the results obtained.
3.1 Apriori Algorithm
Algorithm: Apriori Association Rule
Purpose: To find subsets which are common to at least a
minimum number C (Confidence Threshold) of the itemsets.
Input: Database of Transactions D= {t1, t2, …, tn} Set if Items
I= {I1, I2,…., Ik} Frequent (Large) Itemset L Support,
Confidence.
Output: Association Rule satisfying Support & Confidence
Method:
1. C1 = Itemsets of size one in I;
2. Determine all large itemsets of size 1, L1;
3. i = 1;
4. Repeat
5. i = i + 1;
6. Ci = Apriori-Gen(Li-1);
7. Apriori-Gen (Li-1)
1. Generate candidates of size i+1 from large
itemsets of size i.
2. Join large itemsets of size i if they agree on i-1.
3. Prune candidates who have subsets that are not
large.
8. Count Ci to determine Li;
9. until no more large itemsets found;
Figure 3.1.1 shows the generation of itemsets & frequent
itemsets where the minimum support count is 2. To
generate the association rule from frequent itemset we
use the following rule:
 For each frequent itemset L, find all nonempty
subset of L
 For each nonempty subset of L, write the
association rule S. (L-S) if support count of
L/support count of S >= Minimum Confidence
Fig - 3.1.1 Generation of itemsets and frequent itemsets
The best rules from the itemset {n, o, q} are calculated:
Consider the minimum supportis2andminimumconfidence
is 70%. All non-empty subsets of {n, o, q} are: {n, o}, {n, q}, {o,
q}, {n}, {o}, {q}.
Rule 1:{n, o}⇒{q} Confidence = Support {n, o, q} / Support{n,
o} = 2/2 = 100%
Rule 2:{n, q}⇒{o} Confidence = Support {n, o, q} / Support{n,
q} = 2/3 = 67%
Rule 3:{o, q}⇒{n} Confidence = Support {n, o, q} / Support{o,
q} = 2/2 = 100%

Rule 4:{n}⇒{o, q} Confidence = Support {n, o, q} / Support
{n} = 2/3 = 67%
Rule 5:{o}⇒{n, q} Confidence = Support {n,o,q}/Support{o}
= 2/3 = 67%
Rule 6:{q}⇒{n, o} Confidence = Support{n,o,q}/Support{q}
= 2/3 = 67%
Hence the accepted rules are Rule 1 and Rule 3astheysatisfy
the minimum confidence value.
3.2 Predictive Apriori
Predictive Apriori algorithm was proposed by [12]. This
algorithm use larger support and traded with higher
confidence, and calculate the expected accuracy in Bayesian
framework. This algorithm is applied by using a unified
parameter, called predictiveaccuracy,insteadofsupportand
confidence measures as used in apriori. This predictive
accuracy is used to determine association rules. In weka, the
algorithm generates ‘n’ best rules based on user-specified n
value. It includes options such ascar, class indexandnumber
of rules to obtain the results.
The result of this algorithm maximizes the expected
accuracy for future data of association rules. This algorithm
generates association rules as expected number of rules by
user. Scheffer [9] defines this algorithm by: Let D be a
database whose individual records r aregeneratedbyastatic
process P, let X→Y be an association rule. The predictive
accuracy c(X→Y) = Pr(r satisfies Y|r satisfies X) is the
conditional probability of Y ⊆ r given that X ⊆ r when the
distribution of r is governedby P[q]. By itsdefinitionScheffer
calculates the predictive accuracy as E(c(r) c (r),
s(X))=( cB c,s(X) (c (r))P(c)dc)/( B c,S(X) (c (r) )P(c)dc)
where E(c(r) c (r),s(X) ) is the expected predictiveaccuracy
of a rule X→Y. The confidence denotes as c , and the support
of the rule denoted as s(X).
3.3 Filtered Associator
This algorithmisaclassforrunninganarbitraryassociator
on data that has been passed through an arbitrary filter. Like
the associator, the structure of the filter is based exclusively
on the training data and test instances will be processed by
the filter without changing their structure [13]. In Weka it
includes option such as associator with which we can
consider the Apriori, Predictive Apriori, Tertius association
rule and Filtered Associator algorithm, class index and filter
to get the result.
3.4 MapReduce Algorithm
MapReduce is a program model for distributed computing
based on java. MapReduce algorithm contains two
important tasks, namely Map and Reduce[14 ]. Map takes a
set of data and converts it into another set of data, where
individual elements are brokendownintotuples(key/value
pairs). Secondly, reduce task, which takes the output from a
map as an input and combines those data tuples into a
smaller set of tuples. As the sequence of the name
MapReduce implies, the reduce task is always performed
after the map job.
The major advantage of MapReduce is that it allows scaling
data processing over multiple computing nodes. Under the
MapReduce model, the data processingprimitivesarecalled
mappers and reducers. Decomposing a data processing
application into mappers and reducers is sometimes
nontrivial. But, once we write an application in the
MapReduce form, scaling the application to run over
hundreds, thousands, or eventensofthousandsofmachines
in a cluster is merely a configuration change. This simple
scalability is what has attracted many programmers to use
the MapReduce model.
Algorithm
MapReduce algorithm executes in three stages, namely map
stage, shuffle stage, and reduce stage.
o Map stage: The map or mapper’s job is to process the
input data. Generally the input data file is stored in the
Hadoop file system (HDFS) and is passedtothemapper
function line by line. The mapper processes the data
and creates several small chunks of data.
o Reduce stage: This stage is the combination of
job is to process the data that comes from the mapper.
After processing, it produces a new set of output,which
will be stored in the HDFS.
 During a MapReduce job, Hadoop sends the Map
and Reduce tasks to the appropriate servers in the
cluster.
 The framework manages all the details of data-
passing such as issuing tasks, verifying task
completion, and copying data around the cluster
between the nodes.
 Most of the computing takes place on nodes with
data on local disks that reduces the network traffic.
 After completion of the given tasks, the cluster
collects and reduces the data to form an
appropriate result, and sends it back totheHadoop
server.
4. EXPERIMENTAL RESULTS
4.1 Tools Used
The task of data mining is carried with the help of data
mining tools. Some of them are Waikato Environment for
Knowledge Analysis (WEKA), Orange, RapidMiner (also
known as YALE), Konstanz Information Miner (KNIME),
Sisence, Apache Mahout, Datamelt, Oracle, Rattle, Apache
the Shuffle stage and the Reduce stage. The Reducer’s

Hadoop etc. In this work, we used WEKA for association rule
mining using three algorithms on both low dimensional and
high dimensional datasets and studied the parallel map
reduce algorithm in Hadoop framework.
4.2 Result Analysis
Apriori Algorithm
With support=0.1
Confidence=0.9
Low Dimensional Dataset
High Dimensional Dataset
Filtered Associator Algorithm
ClassIndex=-1
Associator is Apriori
Filter used is MultiFilter
Low Dimensioanl Dataset
High Dimensional Dataset
Predictive Apriori Algorithm
Car value is taken false
ClassIndex=-1
numRules=100
Low Dimensional Dataset
High Dimensional dataset
Fig- 4.2.1: Association mining results of three algorithms
Fig- 4.2.2: screen shot showing the status of map reducing
Fig- 4.2.3: screen shot for final results of mapreduce
framework
Fig- 4.2.4: Shuffle and Reduce stages of mapreduce
framework
Fig- 4.2.5: clusters formed with mapreduce framework

3. CONCLUSION
In this paper, we have discusses various association mining
algorithms such as Apriori,FilteredassociaterandPredictive
Apriori algorithms. We have analyzed the frequent itemsets
generated and number of cycles performed on both low and
high dimensional datasets. We observed that these
algorithms work well with low-dimensional datasets but
fails with high-dimensional data sets due to curse of
dimensionality. We conclude that Parallel map reduce
algorithm is best for associationminingonhigh-dimensional
datasets.
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BIOGRAPHIES
Anuradha Surabhi, Associate Professor
in Dept. of CSE, Aurora’s Technological
& Research Institute has 15 year of
teaching experience and interested in
the area of data mining and Big data
Analytics.
Niharika A, is more enthusiasticperson
to do research in the field of data
mining and doing graduation in B.Tech
CSE from Aurora’s Technological &
Research Institute.
Manaswini Goparaju, studying B.Tech
CSE from Aurora’s Technological &
Research Institute. She is more
passionate in the field of Teaching and
interested in doing Ph.D from a
reputed University.
Manisha Nekkanti, doing her
graduation in B.Tech CSE from
Aurora’s Technological & Research
Institute. She is more interested in
Ethical Hacking and wanted to achieve
a successful life with higher education
and good career goals.

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