In large scale datasets, mining frequent itemsets using existing parallel mining algorithm is to balance the load by distributing such enormous data between collections of computers. But we identify high performance issue in existing mining algorithms [1]. To handle this problem, we introduce a new approach called data partitioning using Map Reduce programming model.In our proposed system, we have introduced new technique called frequent itemset ultrametric tree rather than conservative FP-trees. An investigational outcome tells us that, eradicating redundant transaction results in improving the performance by reducing computing loads.

1. IDL - International Digital Library Of
Technology & Research
Volume 1, Issue 5, May 2017 Available at: www.dbpublications.org
International e-Journal For Technology And Research-2017
IDL - International Digital Library 1|P a g e Copyright@IDL-2017
Web Oriented FIM for large scale dataset using
Hadoop
Mrs. Supriya C
PG Scholar
Department of Computer Science and Engineering
C.M.R.I.T, Bangalore, Karnataka, India
supriyakuppur@gmail.com
Abstract: In large scale datasets, mining frequent
itemsets using existing parallel mining algorithm is to
balance the load by distributing such enormous data
between collections of computers. But we identify
high performance issue in existing mining algorithms
[1]. To handle this problem, we introduce a new
approach called data partitioning using Map Reduce
programming model.In our proposed system, we have
introduced new technique called frequent itemset
ultrametric tree rather than conservative FP-trees. An
investigational outcome tells us that, eradicating
redundant transaction results in improving the
performance by reducing computing loads.
Keywords: Frequent Itemset, MapReduce, Data
partitioning, parallel computing, load balance
1 INTRODUCTION
Big data is an emerging technology in modern world.
It is a greater amount of data, which is hard to process
using traditional data processing techniques or
software‟s. Major challenges in big data are
information safekeeping, distribution, searching,
revelation, querying, updating such data. Data
analyzation is another big apprehension need to
concentrate while dealing with big data. It involves
data which is formed by different types of data and
applications like social media data, online auctions.
Data is differentiated into 3 major types‟ structured,
unstructured and semi-structured data. It also defines 3
major V‟s Volume, Velocity, and Variety which gives
us apparent notion on what is big data.
Now a day‟s data is growing very fast, consider an
example: many hospitals have trillions of data facets
of ECG data. Twitter alone collects around 170million
temporal data, every now and then, serves as much as
200million queries/day. Most important limitations
with the existing systems are handling larger datasets;
our databases can handle only structured data but not
varieties of data, fault tolerance, scalability. That‟s
why big data consign an important role in these days.

2. IDL - International Digital Library Of
Technology & Research
Volume 1, Issue 5, May 2017 Available at: www.dbpublications.org
International e-Journal For Technology And Research-2017
IDL - International Digital Library 2|P a g e Copyright@IDL-2017
Considering bulky datasets, it is not able to handle all
with a single machine. So data need to be distributed
and processing it Parallely amongst clusters of nodes,
which is a foremost challenge. To handle this scenario
we need to design a distributed storage system. In big
data, this can be conceded by a system called Hadoop
– stores and processing big data. It includes 2
important techniques called HDFS (storing big data)
and MapReduce framework (processing big data). Big
data process deals with 3 different techniques data
ingestion, data storage, and data analysis.
If data is distributed it is tough to find the locality of
such files in view of bigger datasets. Better solution to
this problem is to follow Master-Slave architecture, in
which single machine acts as a „Master‟ and remaining
machines are treated as „Slave‟. Master knows the
location of file being stored on different Slave
machines. So whenever a client sends a request,
Master machine processes it by finding out the
requested file in any of the underlined slave machines.
Hadoop follows same architecture.
2OBJECTIVES
The main goal of the project is to eliminate the
redundant transactions on Hadoop nodes to improve the
performance and this can be achieved by reducing the
computing and networking load. It mainly gives
attention to grouping highly significant transactions into
a data partitioning. In the area of big data processing,
MR framework has been used to develop parallel data
mining algorithms which includes FIM, FP-growth [3]
based, some ARM.
Compared with the traditional system, modern
distributed systems tries to achieve high efficiency and
scalability when distributed data is been executed in a
large scale clusters. Many algorithms have been defined
to process FIM, built in Hadoop which aims at
balancing the load by equally distributed [4] among
nodes. When such data is divided into different parts
need to maintain the connection between the data thus it
leads poor data locality and Parallely it increases data
shuffling costs and network overhead. In order to
improve data locality in this we are introducing a
parallel FIM technique, where bulk of data is distributed
across Hadoop clusters.
In this paper they have implemented FIM on Hadoop
[10] clusters using Map Reduce framework. This project
aims is to boost the performance of parallel FIM on
Hadoop clusters and this can be achieved with the help
of Map and Reduce job.
3 METHODOLOGY
Traditional mining algorithms [2] are not enough to
handle large data sets. Thus we have introduced a new
data partitioning technique. Parallel computing [7] is
one more method which we have introduced here to
compute the redundant transactions parallely. So that
we can achieve better performance compared with the
traditional mining algorithms.

3. IDL - International Digital Library Of
Technology & Research
Volume 1, Issue 5, May 2017 Available at: www.dbpublications.org
International e-Journal For Technology And Research-2017
IDL - International Digital Library 3|P a g e Copyright@IDL-2017
Fig 3.1 System Architecture: High Level View
In proposed system, considering old parallel mining
and new mining algorithm using Hadoop technique
shows that how much processing time is acquired by
each of system. In which Hadoop gives us better
modules to achieve this and illustration of whole
system is depicted briefly in the Fig 3.1.
4 IMPLEMENTATION
In this project, we are trying to show how to achieve
better performance measure by comparing existing
parallel mining algorithm with data partitioning
system using some cluster algorithms. First we will
load large datasets into HDFS [6], once it is uploaded
into the main web server where parallel FIM [5]
application is running. Based on the minimum support,
it partitions the data among 2 different servers and
runs two map reduce jobs. Finally, result will be sent
back to the main server which conducts another map
and reduce job to mining further frequent itemsets.
Thus here we are running 3 map and reduce job.
Step1 Scans transaction DB: In this step first we
will scan the transaction database to retrieve the
frequent itemsets and call is as frequent 1-itemsets.
And each set consist of key and value pair.
Step 2 Organizing frequent 1-itemsetsFlist: Based
on the frequent 1-itemsetsfrequency it sorts in a
decreasing order fashion call it as Flist.
Step 3 FIU-Tree: It performs with 2 Map and Reduce
phase.
 Mapper:From step2 we got Flist, here
Mappers process Flist and finally will
produce output as a set of <key, value> pair.
 Reducer: Each reducer instance is assigned
to process one or more group-dependent sub-
datasets one by one. For each sub-datasets,
the reducer instance builds a local FP-tree.
During the recursive process, it may output
discovered patterns.
Step 4: Accumulating: the outcomes which are
generated in Step.3are combined to produce final
result.
5 OUTCOMES
Bringing together both new parallel mining algorithm
and data partitioning yields to better performance by
comparing with the traditional mining algorithms like
Apriori , MLFPT [9] etc. which is showcased in below
graph.

4. IDL - International Digital Library Of
Technology & Research
Volume 1, Issue 5, May 2017 Available at: www.dbpublications.org
International e-Journal For Technology And Research-2017
IDL - International Digital Library 4|P a g e Copyright@IDL-2017
Fig 5.1 Effects of minimum support
Fig 5.2 Speed up performance
CONCLUSION AND FUTURE SCOPE
Any area if we consider can realize huge level of
records will be generated in a fraction of a second.
Processing such info “Apache Hadoop” provides
different framework like MapReduce etc. In
Traditional parallel mining algorithms for frequent
itemset mining it takes more time to process such data,
system performance and balancing the load was major
challenges. This experiment introduces a new parallel
mining algorithm called FIUT using Map Reduce
programming paradigm; it divides the input data
across multiple Hadoop nodes and start doing parallel
excavating to generate frequent itemset. This data
partitioning technique not only improves the
performance of a system but also balance the load.
In future it can be validated with another emerging
technology introduced by Apache Hadoop is Apache
Spark [6]. It is a cluster computing technology [8],
which is faster than Map Reduce. It uses python as a
programming language, where Map Reduce uses Java.
Python requires less number of codes to write. Thus it
improves processing speed.
ACKNOWLEDGEMENT
I would also like to thank Mrs. Swathi,
Assoc. Professor andHOD, Department of Computer
Science and Engineering, CMRIT, Bangalore who
shared her opinions and experiences through which I
received the required information crucial for the
project.
REFERENCES
[1].Fast Parallel ARM without Candidacy generation.
Osmar R. ZaYane, Mohammad El-Hajj , Paul Lu.
Canada : IEEE, 2001. 7695-1 119-8.
[2]. Cloud Data Mining based on Association Rule.
CH.Sekhar, S ReshmaAnjum. 2091-2094,
AndraPradesh : International journal of computer
science and information technology, 2014, Vol. 5 (2).
09759646.
[3]. An enhanced FP growth based on MapReduce for
mining association rules. ARKAN A. G. AL-
HAMODI, SONGFENG LU, YAHYA E. A. AL-
SALHI. China : IJDKP, 2016, Vol. 6.

5. IDL - International Digital Library Of
Technology & Research
Volume 1, Issue 5, May 2017 Available at: www.dbpublications.org
International e-Journal For Technology And Research-2017
IDL - International Digital Library 5|P a g e Copyright@IDL-2017
[4]. Novel Data-Distribution Technique for Hadoop in
Heterogeneous Cloud Environments.
VrushaliUbarhande, Alina-
MadalinaPopescu,HoracioGonz ́alez–V ́elez. Ireland :
International Conference on complex intelligent and
software sensitive systems, 2015, Vol. 15. 978-1-
4799-8870-9.
[5]. An Improved MapReduce Algorithm for Mining
Closed Frequent Itemsets. YaronGonen, Ehud Gudes.
Israel : International Conference on Software Science,
Technology and Engineering, 2016. 978-1-5090-1018-
9.
[6]. Big Data Management Processing with Hadoop
MapReduce and Spark Technology: A Comparison.
AnkushVerma, AshikHussainMansuri ,Dr. Neelesh
Jain. 16, Rajasthan : CDAN, 2016.
[7] Deep Parallelization of Parallel FP-Growth Using
Parent-Child MapReduce. AdetokunboMakanju, Zahra
Farzanyar, Aijun An, Nick Cercone,ZaneZhenhua Hu,
Yonggang Hu. Canada : IEEE, 2016.
[8] A distributed frequent itemset mining algorithm
using Spark for Big Data analytics. Feng Zhang,
Yunlong Ma, Min Liu. New York : Springer, 2015.
[9] Review:Association Rule for Distributed Data.
BhagyashriWaghamare, Bharat Tidke. India :
ISCSCN. 2249-5789.
[10] H2Hadoop: Improving Hadoop Performance
using the Metadata of Related Jobs.
HamoudAlshammari, Jeongkyu Lee and Hassan
Bajwa. TCC-2015-11-0399, s.l. : IEEE, 2015.