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International Journal of Emerging Technology and Advanced Engineering
Website: www.ijetae.com (ISSN 2250-2459, ISO 9001:2008 Certified Journal, Volume 4, Issue 3, March 2014)
Data Mining Techniques in Parallel and Distributed
Environment- A Comprehensive Survey
Shraddha Masih1, Sanjay Tanwani2
1,2
School of Computer Science & IT, DAVV, Indore, India
Parallel computing techniques took a boost with the
advent of multi core CPUs and cheaper GPUs. A
combination of CPU and GPU resulted in multi fold
performance benefit.
Last trend is distributed data mining where data
mining techniques were applied in different distributed
computing paradigms like peer to peer, clusters, grids
and cloud environment [1].
Abstract— Distributed sources of voluminous data have
raised the need of distributed data mining. Conventional
data mining techniques works well on structured data
which is clean, pre-processed and properly arranged either
in the form of structured files, databases or data warehouse.
These techniques are based upon centralised data store
however they have several limitations in distributed
scenario where the data is scattered in different
geographical locations on data servers all across the
network. It becomes a costly affair to accumulate huge data
on a centralised node in real time. To overcome these
limitations, application of distributed data mining
techniques has become essential. This paper describes
various data mining tools and techniques that can be used
in distributed environment. Different algorithmic and
architectural approaches are followed in various distributed
mining techniques. Latest approaches in distributed data
mining are explored. Various research issues and challenges
in the field of distributed data mining are also discussed.
II. ABOUT DATA MINING
In this competitive world, top level management needs
to take right decisions at right time for giving better
service to customers, and to provide better organizational
image. Decisions based on better analysis results in
increasing profit and decreasing loss. For doing so,
management is dependent on better analytical and data
mining services.
Abbreviations: KDD-Knowledge discovery in databases,
ARM- Association rule mining, DDM- Distributed Data
Mining, GPU-Graphical processing Unit
I. INTRODUCTION
Organizations need to accumulate vast and growing
amounts of data in different databases. This data may be
either transactional data like sales, inventory, payroll,
accounting etc. or analytical data that is helpful in
decision support systems. For utilizing this data, it must
be analyzed thoroughly. Many analytical tools are
available in market. Data mining techniques also come in
the category of analytical systems that help to give
insight into hidden information. It can be helpful to find
patterns, relationships and categories of data [2].
Data mining is considered as a part of KDD process.
Main steps of KDD include data accumulation, cleaning,
pre-processing, storing, mining and finally representing
the patterns in a presentable format.
In last twenty years lot of research has been done on
improvising performance of data mining techniques.
From past to present, three different trends have been
observed. The first trend is based on centralized approach
where all data needs to be stored on a central node.
Mostly sequential algorithms were a part of this
approach.
The second trend was observed in terms of
parallelizing centralized algorithms. Two main
approaches were used for parallelization: Task
parallelism and Data parallelism.
Fig.1Data Mining Process
Data mining offers a wide range of algorithms used for
analysis, pattern discovery and prediction. It includes
techniques such as association rule mining, decision
trees, regression, support vector machines and many
more.
Data mining techniques evolved as a requirement
when enormous data started accumulating in digital
format. A wide variety of profitable solutions are hidden
inside this wide pool of data.
The existing data mining algorithms can work in three
different computing environments:
 Centralised
 Parallel
 Distributed
A. Centralised Approach for Data Mining
Organizations may have multiple repositories of
transactional data depending on the location of their
office.
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International Journal of Emerging Technology and Advanced Engineering
Website: www.ijetae.com (ISSN 2250-2459, ISO 9001:2008 Certified Journal, Volume 4, Issue 3, March 2014)
In centralized approach, data is extracted and
accumulated on a centralized store after cleaning and pre
-processing. From this central store, task relevant data is
selected and mining techniques are applied.
Initially, data mining techniques were restricted to
centralized processing [3],[4][5][6][7].
Data mining algorithms are helpful in digging out
hidden previously unknown information from existing
data.
Xindong Wu et al.[2] did a survey in 2007 and
presented top 10 algorithms mostly used by the analysts
of the world. The algorithms were rated on the basis of
their popularity, performance and utility. The centralised
algorithms that were considered to be most influential are
C4.5, k-Means, SVM, Apriori, EM, PageRank,
AdaBoost, kNN, Naive Bayes, and CART. [8,9,10,11]. A
brief about these algorithms is presented below:
Table.1
Top Algorithms of Data Mining
Data
mining
Technique
Association Rule
Centralized Algorithms
Feature
A Priori
Botttom-Up Approach: Requires n scans of database for finding association rules upto n
itemsets.
Botttom-Up with Top Down Approach: Requires early termination if big itemsets are found to
be frequent while doing top down comparison.
Requires database scan only twice since the rules are derived from the Frequent Pattern Tree.
Hill climbing method of clustering for creating K clusters.
Mathematical modelling method based on random phenomenon.
Decision tree based method for classification from which rules can also be derived. Based upon
Gini Diversity Index. Multiway tree can be generated.
Generates binary decision tree. Information based method for splitting nodes.
Derives classification function to distinguish different classes of training dataset
It is not based on exact match for classification. It finds a group of k objects in the training set
that are closest to the test object.
Supervised classification method based on comparing score with threshold.
Ensemble learning method that combines many weak rules for creating accurate prediction
rules.
Search Ranking algorithm based on web hyperlinks of web pages.
Pincer Search
Clustering
Classification
FP Tree Growth
K Means
EM Algorithm
C4.5
CART
SVM
kNN
Prediction
Niave Bayes
AdaBoost
Others
PageRank
General purpose programming can also be done on
Graphical Processing Units where multi cores can be
exploited for highly parallel processing. Many data
mining algorithms have been specifically designed in
CUDA and shows drastic improvement in performance.
Parallel programming is incomplete without
discussing on the recent approach called Map Reduce
[17]. It can process large sized data sets in a highly
parallel manner. Map Reduce was introduced by
Google in 2004. Map Reduce has become the most
popular framework for mining large-scale datasets in
parallel as well as distributed environment. Different
computing
environments’
require
different
programming paradigms depending upon the problem
type. As data mining techniques are data and compute
intensive both, it can be exploited better by using any
one or a combination of parallel programming
approaches given in next table:
B. Parallel Approach for Data Mining
Many scientific and compute intensive and large
problems can be better solved using parallel
programming approach. Data mining can be executed
in a highly parallel environment over multiple
processors. Parallel implementations of data mining
algorithms can be distinguished on the basis of task
parallel and data-parallel approaches [16].
Modern Programming languages are also structured
so as to efficiently utilize novel architectures. There
exist dedicated parallel programming paradigms for
parallelizing the algorithms over multiprocessor and
networked systems. OpenMP and MPI are exclusively
used to achieve shared and distributed memory
parallelization. [24, 25].
CUDA is a programming language that is designed
for programming on NVIDIA GPUs [23]. CUDA offers
a data parallel programming model. In CUDA, threads
access different memories of GPU.
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International Journal of Emerging Technology and Advanced Engineering
Website: www.ijetae.com (ISSN 2250-2459, ISO 9001:2008 Certified Journal, Volume 4, Issue 3, March 2014)
Table 2.
Parallel approaches
MPI
OpenMP
CUDA
MapReduce
A framework for distributedmemory
Parallelism. It is a concept not a
software.
A framework for
threaded
parallelism.
Shared memory model.
A parallel programming model for
multiprocessing environments in
GPUS.
Multithreaded framework.
threads are assigned either a
map or a reduce task
Multiple tasks
run concurrently across separate
nodes.
Multiple threads run
concurrently whereUsual
mapping is 1 thread : 1 core
Multiple lightweight threads run
concurrently on each block of GPU.
MapReduce library expresses
the computation as two
functions: map and reduce
Each task has its own private
memory
Shared memory
is accessible to all threads
Threads access shared memory as
well as registers. Individual registers
for individual threads.
Each map task runs in slave
nodes. Reduce task runs on
master node.
On Distributed
network
On Multi-core
processors
Specially designed for GPUs
On multicore CPU, GPU,
GRIDs and on cloud.
Message based Message Passing
Send and Receive
Directive based (C/C++)#
pragma omp directives
Kernel function runs on GPU.
Based on key-value pair
Flexible and expressive: Can be
used on a wider range of
problems than OpenMP
Each process has its own local
variables
Easier to program
and debug than MPI
C- Extension so much easier for Cprogrammers.
Generally used when the data
size is very large.
Directives can be added
incrementally
Kernel function has its own local
variables.
Map task is highly scalable and
works on distributed data.
Future Scope in Parallel Data mining
With the availability of cheaper, highly parallel GPUs
in market, lot of research is done in parallelizing data
mining algorithms for these devices. GPUMiner, is a
novel parallel data mining system that utilizes newgeneration graphics processing units (GPUs). This
system relies on the massively multi-threaded SIMD
architecture. [26].
Various data mining algorithms including association
rules, clustering and classification have been modified
for parallel processing architectures [27, 28, 29, 30, 31,
32]. Parallel mining on multidimensional data storage
have also been explored by S.Goil and A. Choudahary.
[33].
Jin, Ruoming, Ge Yang, and Gagan Agrawal focused
on shared memory parallelization of data mining
algorithms. They parallelized data mining algorithms,
and their technique applied to large number of data
mining problems. They proposed a reduction-objectbased interface for specifying a data mining algorithm
[63].
We present identified future scopes in the field of
parallel data mining.
i. CPU+GPU combination can be used for performance
enhancement in compute intensive tasks [79]. CUDA
can make computations on a single computer run
faster by using its CPU+GPU combination.
ii. Using GPUs in clusters of computers can achieve large
scale, cost-effective, and power efficient solution of
data mining [80].
iii.
Lot of scope is there in developing map-reduce-like
models for programming in heterogeneous CPU-GPU
clusters.
C. Distributed Approach for Data Mining
The larger amount of data you store on a single
machine, the longer it takes to access. With time the
amount of data grows so large that firing analytical
queries on these data becomes very time consuming.
By dividing the data and distributing it on several
machines, you need strong indexing techniques to point
at the appropriate servers. Distributed approach for data
mining is useful when the data sources are at multiple
sites. Data extraction, cleaning, pre processing and
integrating consumes majority of time thereby affecting
the analysis process. When it comes to time critical
applications, this delay cannot be tolerated. Thus, there
exists a requirement to mine such data in a distributed
manner.
D. Distributed Data mining Challenges
Distributed data: As the dimension of an organization
grows, managing data is convenient when distributed as
per the location or functionality.
Storing and managing distributed data is a challenge
especially when it has to be reused for global processing.
Wilford-Rivera Ingrid [11] have explored the methods
to apply data mining on distributed databases.
BIGDATA: Big data is a collection of structured and
unstructured data sets that so large and complex that it
becomes difficult to process using conventional database
management tools.
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BIGDATA come from variant sources like web logs,
click histories, e-commerce applications, Retail purchase
histories, bank and credit card transactions, social
networking and media, mobile devices call & text data,
networked devices and sensors.
Traditional data mining techniques are not up to the
mark for processing and analyzing Big Data in a time &
cost-efficient manner.
For such applications, the Map Reduce frame work
has recently attracted a lot of attention. Google’s
MapReduce and open-source equivalent Hadoop is a
powerful tool for building such applications.
The main benefit of Hadoop is that it takes advantage
of distributed processing and is scalable and fault
tolerant.
Kyuseok Shim [17] applied parallel programming
method MapReduce that can be used for many machine
learning applications. In this paper, MapReduce
framework based on Hadoop is discussed, and the stateof-the-art in MapReduce algorithms for data mining is
presented.
Unstructured and Complex Data: Unstructured data is
the one that cannot be retrieved through SQL. It is
generally non tabular and does not have any pattern [14].
A related new style of database called NoSQL (Not
Only SQL) has emerged now a days. NoSQL
encompasses wide variety of data management
techniques but exploring NoSQL data for analysis is still
a challenging job. Main NoSQL databases [15] currently
available include: HBase, Cassandra, MarkLogic,
Aerospike and MongoDB.
Distributed operations: Distributed queries are fired
when an application distributes its tasks among different
computers in a network. The challenge is to apply data
mining techniques in a distributed fashion with
underlying consideration of reducing overall data transfer
over the network. Service-oriented architecture can be
exploited for the implementation of data mining in
distributed environments [18,19].
Data privacy and security: Automated data mining in
distributed environments raises serious issues in terms of
data privacy, security, and governance. Various
algorithms have been modified so as to retain privacy in
distributed environment.[37,38,39,40].
This method requires lot of synchronization
overheads. ii. Centralized ensemble methods: This
method generates local models and transmits them to a
central site (asynchronously). The central site forms a
combined global model. These methods require only a
single round of message passing, resulting in modest
synchronization requirements [41].
First, we present different distributed data mining
techniques proposed by researchers that have helped in
enhancing performance of basic data mining techniques.
We included association rules, classification and
clustering in our study.
Distributed Association Rules:
Association rule mining has been studied intensively
in last 20 years. Hundreds of algorithms are proposed till
date but the recent focus is on mining association rules in
a distributed fashion. A-priori, pincer-search, FP- Tree
growth algorithms have been implemented in different
ways using different data structures. Moving toward
distributed approach, researchers have tried to parallelize
existing algorithms and proposed CD-Count distribution,
FDM-Fast distributed algorithm, FPM-Frequent pattern
mining and DDM-Distributed data mining.
Later, the researchers started optimizing the ARM
algorithms by using hybrid methods. Assaf Schuster, Ran
Wolff, Dan Trock used a combination of sampling &
storing in vertical trie data structure and further mined
this data structure using DDM method[42].
A Tree based Algorithm for Generating of Frequent
Item Sets was also proposed which uses Pattern Count
Tree for representing the database. [44].
A parallel algorithm for data mining of association
rules on shared-memory multiprocessors was tested for
optimizations of fast frequency computation. Degree of
parallelism, synchronization, and data locality issues
have also been discussed for shared memory systems
[46].
D-ARM algorithm [47] proposed by Assaf Schuster
outperformed on a number of computing nodes with less
communication cost.
Distributed Classification Algorithms:
Standard classification algorithms include C4.5, ID3,
SLIQ and SPRINT. Many researchers have put efforts on
parallel implementation of these algorithms [48].
An algorithm for classification on multi relational data
with handling of missing values and less communication
cost have been proposed by Anna Atramentov [49].
Current database systems are mostly of distributed
nature. Performing classification on this distributed data
is a highly challenging job. Problem of inducing decision
trees in a large distributed network of databases requires
an algorithm that can reduce the communication
overhead by sending just a fraction of the statistical data
[50].
E. Distributed data mining algorithms
With fast growing business intelligence market,
exponential increase in the amount of data and
distributed locations of data, there has raised a
requirement of distributed data mining. The distribution
may be either of computation or of data.
For distributing the mining task, any one of two
strategies can be used: i. Message passing among nodes
or processors: Nodes in a distributed system
communicate via messages.
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In distributed environment, data is distributed and
every data server has a partial set of the data. A
classification algorithm for vertically partitioned data
assumes that local classifiers can be constructed locally.
These local classifiers can be used to support decision
making at each location. Global classifier can then be
constructed having access to the entire feature set [51].
A K-Means based P2P mining technique [60] for
clustering homogeneously distributed data works by
communicating with the neighbouring nodes in
asynchronous manner. The work also offers theoretical
analysis of the algorithm that bounds the error in the
distributed clustering process compared to the centralized
approach.
New cast model of computation is used by Wojtek
Kowalczyk, Mark Jelasity, and A.E. Eiben efficiently
mines data over P2P overlay networks [61].
Distributed Clustering Algorithms:
S. Datta, C. Giannella, and H. Kargupta [52] presented
K Means algorithm for clustering on large data
distributed over dynamic network. This algorithm is
robust to network change and does not require global
synchronization. It is based upon local synchronization.
S. Bandyopadhyay, C. Giannella, U. Maulik, H.
Kargupta , K. Liu, and S. Datta[53] described a technique
for clustering homogeneously distributed data in a peerto-peer environment. The proposed technique is based on
the principles of the K-Means algorithm. In this
technique, the neighbouring nodes communicate in a
localized asynchronous manner.
Clustering process can be optimised by sending best
representatives to a server site. The process can be very
efficient, because determining local representatives can
be carried out quickly and independently from each
other. Based on the most suitable local representatives,
global clustering can be done efficiently [54, 55]. A
novel distributed clustering algorithm KDEC uses
Sampling based methods for non-parametric kernel
density estimation on local sites. It also takes into
account the issues of privacy and communication costs
that arise in a distributed environment [56].
GRIDS:
GRID consists of many tightly coupled perhaps
geographically distributed heterogeneous computers
which are made to work together on either single or
related problems. Grids are required by professional
communities who need to access remote resources,
distributed datasets, and for large scale data analyses.
Grid can play a significant role in providing an effective
computational support for distributed data mining
applications.
Cannataro M, Congiusta A, Pugliese A, Talia D,
Trunfio P. [62]designed a system called Knowledge Grid.
Their work describes the Knowledge Grid framework
and presents the toolset provided by the Knowledge Grid
for implementing distributed data mining on GRID.
A three layered architecture called Data Mining Grid
system was proposed to enable creation of grids
dedicated to data mining tasks [67].Globus toolkit 4 is
used as a middleware between upper and lower layers.
Grid can offer an infrastructure for supporting
decentralized and parallel data analysis. Service oriented
grid computing can allow the end-users to focus on the
knowledge discovery process without worrying about the
details of grid infrastructure [68]. Data mining services
on grids can now be accesses through web services also
[69].
A system called KNOWLEDGE GRID framework
presents the toolset provided implementing distributed
knowledge discovery. Tool provides the facility of
starting from searching grid resources, and then finally
executing the resulting data mining process on a grid
[70].
Intra Grid based data mining tool DMGCE is
developed with the use of competitive directed acyclic
graphs in a heterogeneous computing environment. It
works on a dynamic scheduling framework. In this
framework, reuse of existing DM algorithms is achieved
by encapsulating them into agents [71].
F. Data Mining in Distributed Computing Environments
PEER to PEER systems:
Idea behind peer to peer computing is to create a group
of computers connected together to combine their
computing and processing abilities to solve complex
problems. Each computer has equal capability. This
architecture is widely used for enormous data storage,
scientific computations and data analytics.
DDM applications and algorithms for Peer to
per
environments, are described by Datta and Souptik where
both exact and approximate local P2P data mining
algorithms work in a decentralized and communicationefficient manner[57].
Wolff, Ran, and Assaf Schuster proposed an
Association rule mining in peer-to-peer systems[58].
They presented an algorithm by which every node in the
system can find about equal confidence level though they
work on data partitions.
Schuster, Assaf, and Ran Wolff [59] also presented a
set of new algorithms that solve the Distributed
Association Rule Mining problem. These algorithms are
very efficient and also extremely robust on skewed and
imbalanced data partitions.
CLUSTERS:
All machines in a cluster are homogeneous and work
as a single unit. The computers in the cluster are
normally contained in a single location.
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Computers in a cluster works as a single computing
resource and are connected by high speed networks. A
cluster is a cheaper alternative to a single high
performance processor system like super computers.
Clusters are ideal for users who need to run similar jobs
as in case of data mining.
Tree data structure is generally used to compress
database. Such tree structures can be efficiently mined
using frequent pattern growth methodology. PC cluster
based framework can be used for tree mining resulting in
improved support counting procedure [72]. However
there is a problem when FP-tree cannot fit into the
memory. A parallel execution of FP-growth using PC
Cluster is implemented for execution efficiency on
shared-nothing environment [73]. Performance of FPTree decreases with the increase in size of database. For
handling this problem a combination of parallel and
distributed techniques can be applied. Use of Tid setbased Parallel FP-tree (TPFP-tree) and Balanced Tid setbased Parallel FP-tree (BTP-tree) for frequent pattern
mining on PC Clusters and multi-cluster grids shortens
the execution time significantly [74].
Recently, a framework called MATE-CG is proposed
that uses a Map Reduce-Like Framework Data-Intensive
Computations on a heterogeneous cluster of multi-core
CPUs and many-core GPUs [75].
Data mining on very large datasets can be optimised
by using Open source framework called Hadoop. Hadoop
– MapReduce is a highly parallel programming paradigm
that is used by big shots like Yahoo, Facebook, Ebay,
Twitter and many more.
Future Scope in Distributed Data mining:
We have discussed several issues related to distributed
data mining. After carefully examining the current trends,
we propose that data mining techniques in near future
will be oriented towards following areas:
i. Use of Hadoop Mapreduce for large sized
data[81,82]. Hadoop Distributed file system
automatically handles scalability and fault tolerant
issues.
ii. Combining
ETL
tools
like
Mahout,
Sqoop, Flume and Mongo-Hadoop Connector, we
can mine NoSQL Big databases[83].
iii. CUDA a highly parallel programming language
that is designed for GPU can run within Mapreduce
for further improving efficiency of mining
compute-intensive tasks over petabytes of data[84].
iv. Use of G-Hadoop with G-Farm file system, a
MapReduce framework that can be used for largescale distributed computing on distributed data [85].
v. Cloud based techniques for data mining are almost
unexplored so there is lot of scope in this direction.
CLOUD:
Cloud is an infrastructure that provides services and
resources through internet. Main services are
Infrastructure as a service -IAAS-, Platform as a service PAAS and Software as a service -SAAS. Cloud can be
used to utilize virtual resources to perform data and
compute intensive analyses. Data mining computations
can be optimised using parallel programming paradigms
like Hadoop-Mapreduce, CGL-MapReduce, and Dryad.
However, many scientific applications still require low
latency communication mechanisms by runtimes such as
MPI. Different MapReduce implementations of data
mining algorithms have been performed on virtualised
resources on cloud [76].
To efficiently support many important data mining
algorithms in cloud environment, a distributed
framework called GraphLab is recently proposed. It is
graph based extension which is fault tolerant and reduces
network congestion [77].
High performance cloud can be used to mine large
distributed data sets[78]. Sector is a distributed file
system that can be processed by Sphere which is a high
performance parallel data processing engine. Sector and
Sphere are designed for analyzing large data sets using
computer clusters connected with wide area high
performance networks A distributed data mining
application have been developed using Sector and
Sphere.
III. CONCLUSION
Data mining has become more relevant today with the
increase in the amount of data generated every minute.
With issues like increase in size, data distribution,
unstructured data, cleaning and pre-processing and is an
open challenge. Data mining techniques can be speeded
up by proper combination of parallel and distributed
approaches. As data floats on network in distributed
systems, privacy preservation techniques are mandatory
to be applied on every DDM technique. In distributed
scenario, we can get better performance in terms of
memory utilization and speedup if there is utilization of
proper blend of resources.
Lot of advancements in the field of data mining is
observed in last decade. Several network and computing
related bottlenecks still exist. We have addressed many
challenges and recent research areas in the field of
distributed data mining. Distributed data mining has to go
long way for benefitting scientists, academicians and
industries.
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