Books on data mining tend to be either broad and introductory or focus on some very specific technical aspect of the field. In this chapter, we present a comprehensive survey of parallelism techniques for data mining. This chapter presents a survey on largescale parallel and distributed data mining algorithms and systems, serving as an introduction to the rest of this volume. Association rule mining arm is an important core data mining technique to discover patternsrules among items in a large database of variablelength. Gpuaccelerated large scale analytics ren wu, bin zhang, meichun hsu hp laboratories hpl 200938. Parallel and distributed data mining 47 previous pass, and then scans the database, discarding those tuples that do not occur frequently in the database. One difference between these two methodologies is the computing resource management. The data mining system provides all sorts of information about customer response and determining customer groups. Parallel computers offer a potentiaj solution to the computation requirement of this task, provided efficient and scalable parallel algorithms can be designed. Scalable parallel data mining for association rules experts. It is intended to provide only a very quick overview of the extensive and broad topic of parallel computing, as a leadin for the tutorials that follow it.
According to above discussion, big data mining can be efficiently performed via the conventional distributed and mapreduce methodologies. A series of exemplary experiments is used to illustrate the effect such use of parallel resources can have. There are many data mining techniques, such as association rule mining, classification, clustering, sequential pattern mining, etc. Parallel data mining is a growing field that tries to exploit the benefits of parallel computing for min ing large scale databases. Most approaches to parallel data mining focus on distributed execution of multiple experiments, or specific parallel algorithms that are not integrated into popular data mining tools. A comparison of distributed and mapreduce methodologies chih fong tsai,1, wei chao lin 2, and shih we n ke 3 1department of information management, national central university, taiwan 2department of computer science and information engineering, asia university, taiwan. Introduction data mining is a process of nontrivial extraction of implicit, previously unknown, and potentially useful information such as knowledg e rules, constraints, and regularities from data in databases. Data mining is a process of extracting information and patterns, which are pre. The intuition is that for any set of items that occurs frequently, all subsets of that set must also occur frequently. This is the first tutorial in the livermore computing getting started workshop. T1 scalable parallel data mining for association rules. Pdf parallel data mining on graphics processors ali. Parallel data mining pdm deals with tightlycoupled systems including shared memory systems smp, distributedmemory machines dmm, or clus ters of smp.
Several task parallel implementations of backpropagation networks parallel implementations of a selforganizing maps have been implemented for data mining tasks. Pdf parallel data mining case study anthony bagnall. N2 one of the important problems in data mining is discovering association rules from databases of transactions where each transaction consists of a set of items. The parallel and cloud computing platforms are considered a better solution for big data mining. Parallel and distributed data mining guide 2 research. Therefore, it can be helpful while measuring all the factors of the profitable business. N2 one of the important problems in data mining is discovering association rules from databases of transactions. Euihong sam han, george karypis and vipin kumar, proc. Data parallelism is parallelization across multiple processors in parallel computing environments. Pdf parallel data mining on a beowulf cluster peter. Tan,steinbach, kumar introduction to data mining 8052005 1 data mining.
Data mining task primitives we can specify a data mining task in the form of a data mining query. Middleware for highperformance data mining on grid and cloud environments. Using the data directly eliminates errors associated with pdf esti. Another team at hkust and microsoft researchasia has also looked into parallel data mining on gpus fan08. Lecture notes for chapter 3 introduction to data mining. The form of the split used to partition the data depends on the type of the attribute used in the split. Pdf data mining techniques in parallel and distributed. Exploiting modern parallel architectures including fpga, gpu and manycore accelerators for parallel data mining applications. Data distibution algorithm each processor computes support counts for only j c k j p candidates.
Pdf parallel data mining on multicore systems geoffrey. A major issue in data mining is scalability with respect to the very large size of currentgeneration and nextgeneration databases, given the excessively long processing time taken by sequential data mining algorithms on realistic volumes of data. This paper describes the design and implementation on mimd parallel machines of pautoclass, a parallel version of the autoclass system. It is clear that sizable performance gains can be achieved by using a parallel webpage download approach when mining data from the web. Indeed, computational power offered by new gpu architectures remains almost unexplored in most popular desktop data mining tools. Home conferences sc proceedings supercomputing 96 parallel data mining for association rules on sharedmemory multiprocessors. Aug, 2017 scalable parallel data mining algorithms using messagepassing, sharedmemory or hybrid programming paradigms. Data mining some slides courtesy of rich caruana, cornell university ramakrishnan and gehrke. Clementine is a parallel data mining system based on neural nets. Data mining resources on the internet 2020 is a comprehensive listing of data mining resources currently available on the internet. An efficient, scalable, parallel classifier for data mining 1996. Parallel data mining offers new complexity as it incorporates techniques from parallel databases and parallel programming.
Hpa effectively utilizes the whole memory space of all the processors, hence it works well for large scale data mining in a parallel and distributed environment. Performance improvement of data mining in weka through gpu. Research and development work in the area of parallel data mining concerns the study and definition of parallel algorithms, methods, and tools for the extraction of novel, useful. Research and development work in the area of parallel data mining concerns the study and definition of parallel mining architectures, methods, and tools. Parallel data mining pdm 16, 17 is a type of computing architecture in which several processors execute or process an application. Pdf a parallel data mining architecture for massive data sets. The goal of arm is to identify groups of items that most often occur together. Most data mining approaches assume that the data can be provided from a single source. Mining information and knowledge from large databases has been recognized by many researchers as a key research topic in database systems and machine learning, and by many industrial companies as an important area with an opportunity of major revenues. Parallel data mining on graphics processors wenbin fang, ka keung lau, mian lu, xiangye xiao, chi kit lam, philip yang yang, bingsheng he1, qiong luo, pedro v. Pdf a parallel data mining architecture for massive data. A read is counted each time someone views a publication summary such as the title, abstract, and list of authors, clicks on a figure, or views or downloads the fulltext. Holsheimer, kersten, and siebes 1996 developed a parallel data mining tool, data surveyor, that consists of a mining tool and a parallel database server. Therefore if parallel data mining is performed on the big data.
Sometimes, transmitting large amounts of data to a data center is expensive and even impractical. May 17, 2012 most data mining approaches assume that the data can be provided from a single source. Both methodologies require a number of processors or computer nodes to execute some mining tasks in a parallel manner. A scalable parallel classifier for data mining john shafer rakeeh agrawal manish mehta ibm almaden research center 650 harry road, san jose, ca 95120 abstract classification is an important data mining problem. However, there is a diminishing return on adding additional concurrency workers to the download process. Parallel data mining for association rules on sharedmemory. Methods for parallelising data mining task parallelism data parallelism record based attribute based divide and. Parallel computers offer a potential solution to the computation requirement of this task, provided ef. Gspgeneralized sequential pattern mining gsp generalized sequential pattern mining algorithm outline of the method initially, every item in db is a candidate of length1 for each level i. Scalable parallel clustering for data mining on multicomputers. Parallel data mining algorithms for association rules and.
General introduction to data warehousing in parallel with this chapter, you. Therefore if parallel data mining is performed on the big data at the time of its arrival, the models and patterns can be. In this chapter, parallel algorithms for association rule mining and clustering are presented to demonstrate how parallel techniques can be e. This paper presents a parallel data mining application for predictive modelling running on a beowulf style linux cluster. The explosive growth in data collection in business and scienti fic fields has literally forced upon us the need to analyze and mine useful knowledge from it.
Parallel data mining on multicore clusters xiaohong qiu1, geoffrey fox2, huapeng yuan2, seunghee bae2 1research computing, uits 2community grids lab indiana university bloomington in, u. The knowledge or information which is acquired through the data mining process can be made used in any of the following applications market analysis. It also discusses the issues and challenges that must be overcome for designing and implementing successful tools for largescale data mining. Data mining is the automated analysis of large volumes of data, looking for the interesting relationships and knowledge that are implicit in large volumes of data. The concept of association rules in terms of basic algorithms, parallel and distributive algorithms and advanced measures that help determine the value of association rules are discussed. The final chapter discusses algorithms for spatial data mining. For this reason, several data mining systems have been implemented on parallel computing platforms to. Parallel data mining alexandre termier lig laboratory, hadas team alexandre. This book is a series of seventeen edited studentauthored lectures which explore in depth the core of data mining classification, clustering and association rules by offering overviews that include both analysis. Data mining techniques in parallel and distributed. Scalable parallel data mining for association rules1997. This data demands analysis and mining algorithms that are both high performance and robust. Parallel data mining from multicore to cloudy grids community.
Mining with big data or big data mining has become an active research area. Accepted manuscript accepted manuscript big data mining with parallel computing. While highlevel data parallel frameworks, like mapreduce, simplify the design and implementation of largescale data processing systems, they do not naturally or efciently support many important data mining and machine learning algorithms and can lead to inefcient learning systems. However, the more general workflow or dataflow paradigm which is seen in dryad 9 and other mapreduce extensions is always valuable.
Such graph parallel abstractions are expressive and easytoprogram, and have been a popular approach for developing parallel data mining and machine learning algorithms. Parallel data mining algorithms for association rules and clustering. Splits for a continuous attribute a are of the form valuea data warehousing and data mining table of contents objectives context. Conventional data mining techniques works well on structured data. Parallel algorithms in data mining computer science.
Lecture notes in data mining world scientific publishing. Pdf the explosive growth in data collection in business and scienti fic fields has literally forced upon us the need to analyze and mine useful. For this reason, several data mining systems have been implemented on parallel computing platforms to achieve high performance. The optimal threshold is based upon numerous factors including both processing power and connection speed. Parallel data mining from multicore to cloudy grids. It can be applied on regular data structures like arrays and matrices by working on each element in parallel. Data mining or knowledge discovery in databases kdd is the process of analysing large and complex data sets with the purpose of extracting useful and previously unknown knowledge. In this paper, we will describe the parallel formulations of twoimportant data mining algorithms. Salsa is exploring a set of data mining applications implemented in parallel on multicore systems. In sections 3 and 4we turn to some data mining algorithms that parallel require. Pdf strategies for parallel data mining researchgate. It is very difficult using current methodologies and data mining software tools for a single personal computer to efficiently deal with very large datasets. Research and development work in the area of parallel data mining concerns the study and definition of parallel mining.
There is a necessity to developeffective parallel algorithms for various data mining techniques. This survey aims at presenting a much broader view of the area of parallel data mining, by discussing the parallelization of several kinds of. Scalable parallel data mining for association rules. Scalable parallel clustering for data mining on multicomputers d. Data mining refers to the entire process of extracting useful and novel patternsmodels from large datasets.
The intelltgent data distribution algorithm efficiently uses. It focuses on distributing the data across different nodes, which operate on the data in parallel. Mining frameworks the integrated delivery of largescale data mining. Pdf parallel data mining for medical informatics geoffrey. Pdf scalable parallel data mining for association rules. Finally, neural network utility nnu 4 is neural networkbased data mining.
As these types of working factors of data mining, one can clearly understand the actual measurement of the profitability of the business. Methods for parallelising data mining task parallelism data parallelism record based attribute based divide and conquer task queue figure 1. This suggests the importance of parallel data analysis and. Cost effective servers smps provide parallel processing to the mass market. Association rule mining arm is an important core data mining technique to discover patternsrules among items in a large database of variablelength transactions. Abstract as in many fields the data deluge impacts all aspects of life sciences from chemistry data in pubchem. Data mining often is a computing intesive and time requiring process. Need to move transaction data between processors via all to all communication able to deal with large numbers of candidates, but speedups.
Abstract the ever increasing number of cores per chip will be accompanied by a pervasive data deluge whose size will probably increase even faster than cpu core count over the next few years. Anurag srivastava, vineet singh, euihong sam han and vipin kumar. Distributed sources of voluminous data have raised the need of distributed data mining. Definition data mining is the exploration and analysis of large quantities of data in order to discover valid, novel, potentially useful, and ultimately understandable patterns in data. If data was produced from many physically distributed locations like walmart, these methods require a data center which gathers data from distributed locations. The below list of sources is taken from my subject tracer information blog titled data mining resources and is constantly updated with subject tracer bots at the following url.
In this paper, we present two new parallel algorithms for mining association rules. Binary bow of url tokens, whois info, and ip data predict whether site is malicious from these features only, excluding web content faster and safer not to touch malicious content used benign urls from yahoo directory for the nonmalicious class in the dataset this data is also used in two of the following papers. Parallel data mining pdm deals with tightlycoupled systems including sharedmemory systems smp, distributedmemory machines dmm, or clus ters of smp. Sander, and ke yang2 department of computer science and engineering, hong kong university of. Although classification is a well studied problem, most of the current classi. The housing data was the source of the data for the implementation. Without parallelism, it is generally difficult for a single processor system to provide reasonable response time. Due to the huge size of data and amount of computation involved in data mining, highperformance computing is an essential component for any successful largescale data mining application. These primitives allow us to communicate in an interactive manner with the data mining system. A data mining query is defined in terms of data mining task primitives. A study of parallel data mining in a peertopeer network.
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