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基于Hadoop的改進(jìn)的并行Fp-Growth算法

發(fā)布時(shí)間:2018-12-25 07:44
【摘要】:頻繁模式挖掘是數(shù)據(jù)挖掘領(lǐng)域的重要算法。頻繁模式挖掘在事務(wù)數(shù)據(jù)庫(kù)、時(shí)間序列數(shù)據(jù)庫(kù)和許多其他類(lèi)型數(shù)據(jù)庫(kù)的挖掘研究中都得到了廣泛的應(yīng)用。然而,傳統(tǒng)的Frequent-pattern Growth算法(簡(jiǎn)稱Fp-Growth算法)在處理大規(guī)模數(shù)據(jù)時(shí),無(wú)論是存儲(chǔ)上還是計(jì)算上都會(huì)遇到瓶頸,這就需要對(duì)Fp-Growth算法進(jìn)行并行化處理。現(xiàn)有的并行Fp-Growth算法已經(jīng)解決了如何劃分?jǐn)?shù)據(jù)庫(kù)事務(wù)集這一問(wèn)題,并保證了劃分后的事務(wù)集彼此之間相互獨(dú)立,但是現(xiàn)有的并行Fp-Growth算法和對(duì)事務(wù)集進(jìn)行劃分的時(shí)候缺乏了對(duì)負(fù)載均衡的考慮。因此,實(shí)現(xiàn)負(fù)載均衡的并行Fp-Growth算法是本文的主要問(wèn)題。 Hadoop是Apache基金會(huì)下的一個(gè)開(kāi)源的分布式并行編程框架,允許計(jì)算機(jī)集群通過(guò)使用簡(jiǎn)單的編程模型分布式的處理大型數(shù)據(jù)集。Hadoop解決了并行計(jì)算存在的工作調(diào)度、分布式存儲(chǔ)、容錯(cuò)處理、網(wǎng)絡(luò)通訊等問(wèn)題,這就使得開(kāi)發(fā)者只需要關(guān)注算法本身,而系統(tǒng)本身的調(diào)度等問(wèn)題都交由Hadoop處理;谏鲜鲈,所以本文使用Hadoop框架來(lái)實(shí)現(xiàn)并行化的Fp-Growth算法。 本文主要完成了以下兩個(gè)工作,一個(gè)是對(duì)現(xiàn)有的并行Fp-Growth算法提出了改進(jìn),另一個(gè)是將本文提出的并行算法應(yīng)用于頻繁用戶訪問(wèn)序列的挖掘上。首先,本文在國(guó)內(nèi)外的并行Fp-Growth算法的研究基礎(chǔ)之上,利用估算每一個(gè)頻繁項(xiàng)的負(fù)載的方法,對(duì)現(xiàn)有的并行Fp-Growth算法的分組策略進(jìn)行了改進(jìn)。實(shí)驗(yàn)證明本文提出的改進(jìn)的并行Fp-Growth算法優(yōu)于現(xiàn)有的并行Fp-Growth算法,本文提出的算法具有更好的負(fù)載均衡能力和執(zhí)行效率。其次,由于Web服務(wù)器日志上存儲(chǔ)了海量的用戶訪問(wèn)信息,因此,可以從海量的數(shù)據(jù)中發(fā)現(xiàn)那些隱藏起來(lái)的、有價(jià)值的用戶行為信息。所以,本文將提出的算法應(yīng)用于Web日志挖掘這一領(lǐng)域,用來(lái)挖掘頻繁的用戶訪問(wèn)序列;谶@一應(yīng)用方向所得到的結(jié)果可以對(duì)日志的來(lái)源網(wǎng)站提供指導(dǎo)和參考意見(jiàn),具有實(shí)際的應(yīng)用價(jià)值和商業(yè)價(jià)值。
[Abstract]:Frequent pattern mining is an important algorithm in the field of data mining. Frequent pattern mining is widely used in the research of transaction database, time series database and many other kinds of database. However, the traditional Frequent-pattern Growth algorithm (Fp-Growth algorithm for short) will meet the bottleneck in both storage and computation when dealing with large-scale data, which requires parallelization of Fp-Growth algorithm. The existing parallel Fp-Growth algorithms have solved the problem of how to partition database transaction sets, and ensured that the partitioned transaction sets are independent of each other. However, the existing parallel Fp-Growth algorithms and transaction set partitioning lack of load balancing considerations. Therefore, the parallel Fp-Growth algorithm for load balancing is the main problem in this paper. Hadoop is an open source distributed parallel programming framework under the Apache Foundation, which allows computer clusters to deal with large data sets distributed by using simple programming models. Hadoop solves the problem of scheduling and distributed storage in parallel computing. Fault-tolerant processing, network communication and other problems, which make developers only need to pay attention to the algorithm itself, while the system itself scheduling problems are handled by Hadoop. For the above reasons, this paper uses Hadoop framework to implement parallel Fp-Growth algorithm. The main work of this paper is as follows: one is to improve the existing parallel Fp-Growth algorithm, the other is to apply the parallel algorithm to mining frequent user access sequences. Firstly, based on the research of the parallel Fp-Growth algorithm at home and abroad, this paper improves the grouping strategy of the existing parallel Fp-Growth algorithm by using the method of estimating the load of each frequent item. Experiments show that the improved parallel Fp-Growth algorithm is superior to the existing parallel Fp-Growth algorithm, and the proposed algorithm has better load balancing ability and execution efficiency. Secondly, because a large amount of user access information is stored in the Web server log, the hidden and valuable user behavior information can be found from the massive data. Therefore, the proposed algorithm is applied to the field of Web log mining, which is used to mine frequent user access sequences. Based on this application direction, the results can provide guidance and reference for the source websites of the log, and have practical application value and commercial value.
【學(xué)位授予單位】:山東大學(xué)
【學(xué)位級(jí)別】:碩士
【學(xué)位授予年份】:2013
【分類(lèi)號(hào)】:TP338.6

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