一种改进的隐私保护关联规则挖掘算法

隐私保护数据挖掘旨在不访问真实数据的情况下,获得隐藏在数据中的模式或规则。文中介绍隐私保护数据挖掘的几种算法。针对基于部分隐藏随机化回答数据隐私保护挖掘算法(RRPH)中使用多参数扰动数据后,K项集真实支持度计算时间成本高的弊端,提出基于粒度技术的部分隐藏随机化回答数据隐私保护挖掘算法(BRRPH)。该算法在RRPH算法的基础上,使用Bitmap表示技术粒度化数据,把支持度计算的数据处理方式转换成粒度计算的方式,项集真实支持度计算转变成了粒度的交集计算。实验表明在不改变准确性和隐私性的情况下,算法时间效率有大幅度的提高。     

基于FP-GROWTH算法的关联规则挖掘算法研究

互联网世界的数据每年都在成倍增长,但是对用户有用的信息却好像在减少,用户淹没在数据的海洋中,虽然类似于Google这样的搜索引擎可以帮用户找到需要的信息,但是正确率和查全率都不尽如人意。数据挖掘是兴起于20世纪90年代的一项用于决策支持的新技术。FP-GROWTH算法只进行2次数据库扫描。它不使用侯选集,直接压缩数据库成一个频繁模式树,最后通过这棵树生成关联规则。文章研究FP-GROWTH算法理论的同时实现了一个简单算法演示的系统。系统包括算法的执行,对数据库的修改、查询、删除的操作。最后,对FP-GROWTH算法和Apriori算法进行了比较。     

数据关联规则挖掘理论与数据算法的探讨

在众多不确定因素很强的数据中,如何挖掘数据是非常重要的,这时候关联规则挖掘理论就出现了.因此,在数据挖掘的领域中,关联规则有着突出的研究地位.本文从关联规则的产生背景和相关概念做出了论述.     

基于Spark的精准关联规则挖掘算法实现

为在大数据环境中精确地进行关联规则挖掘,基于分布式框架Spark,改进关联规则挖掘算法Apriori,解决使用该算法处理大规模数据时遇到的单机内存资源限制和性能缺陷,同时保证结果准确度。利用开源数据集和海量轨迹数据集评估算法的有效性,实验结果表明:与传统方法相比,改进后的Apriori算法进行规则挖掘能够得到相同准确度的结果,并且通过增加处理节点的数量灵活扩展待挖掘数据规模,从而使关联规则挖掘不再受数据规模限制。     

基于云平台的并行关联规则挖掘算法分析

自云计算技术出现之后,数据挖掘技术取得了突破性发展。数据挖掘系统不仅实现了低成本、高效率运行,并且系统储存空间和系统可扩展性也在不断扩大,大大提高了数据挖掘效率。这里简要阐述了基于云平台的并行关联规则挖掘算法分析的研究意义,并对基于Hadoop的数据挖掘系统和数据算法设计进行了详细介绍。     

应用Aprion算法实现大规模数据库关联规则挖掘的技术研究

随着信息技术的发展,大数据时代的到来,在这种环境下必须进行数据挖掘工作。从大量的应用数据中将潜在的有价值的知识和信息挖掘出来,以便将其应用在实际工作的改进中。目前,数据挖掘的方法有很多,其中关联规则挖掘技术应用比较广泛,这种数据挖掘方式利用Aprion算法,挖掘出置信度和支持度均比较高的关联信息,反映出数据库中的数据相互之间的复杂性和有趣性,进而挖掘出数据之间的有益关联,促进大规模数据库信息挖掘技术的发展,主要从Aprion算法方面分析大规模数据库关联规则挖掘的技术。     

AN INCREMENTAL UPDATING ALGORITHM FOR MINING ASSOCIATION RULES

In this letter,on the basis of Frequent Pattern(FP) tree,the support function to update FP-tree is introduced,then an incremental FP(IFP) algorithm for mining association rules is proposed.IFP algorithm considers not only adding new data into the database but also reducing old data from the database.Furthermore,it can predigest five cases to three case .The algorithm proposed in this letter can avoid generating lots of candidate items,and it is high efficient.     

关联规则中基于模糊遗传算法的研究与改进挖掘技术

网络数据通常以爆炸式的聚集形式出现,导致曾提出的关联规则挖掘方法挖掘性能不好。基于上述原因,提出一种关联规则中基于模糊遗传算法的挖掘方法,此方法通过构建挖掘模型,将关联规则与模糊遗传算法融合在一起,进而给出待挖掘数据的适应度函数,并使用阈值对其进行限制。模糊遗传算法将对适应度函数进行交叉分配和编译分配,以改进方法的隶属度线性状态,获取更为有效的挖掘结果。实验结果表明,改进后的方法挖掘稳定性较强,挖掘精度较高,且挖掘时间较少。     

无线传感器网络中一种改进的分布式加权多维尺度定位算法

本文在无线传感器网络单跳定位误差分析的基础上,分析了多跳节点定位误差的特性,并据此提出针对分布式加权多维尺度定位(Distributed Weighted Multidimensional Scaling,dwMDS)的权值优化算法.在无法获知参考点确切误差的情况下,利用分析出来的克拉美劳下限代替参考点误差并与距离测量误差合并,更准确的反映了多跳定位中的点与点之间的误差,从而有助于设计更优化的权值.仿真结果表明,使用优化权值改进的算法得到的节点定位误差明显减小.     

基于关联规则挖掘的无线电频谱占用预测

无线电频谱占用预测是认知无线电研究中的关键技术之一。实验采用中星世通CS-805 F可搬移监测测向系统对四川省成都市的GSM900上行频段(890~915 MHz)和广播电视业务的部分频段(750~806 MHz)进行了为期24 h的实地监测,针对频谱监测中产生的大量历史数据,选用了部分周期模式的关联规则挖掘方法,挖掘频谱使用中存在的频繁模式,并由信道占用频繁模式生成强关联规则,得到特定业务频段的使用规律,从而实现无线电频谱的占用预测。实验结果表明,该方法在两个业务频段的占用预测均取得了较好的效果,准确率分别可达74.02%和83.98%。另外,实验指出了该算法的敏感参数并进行了简要分析。实验对研究认知无线电设备实施动态频谱接入和提高频谱使用率有一定意义。     

一种优化的特征点匹配算法

为提高三维重建的性能,提出一种优化特征点匹配算法.首先,通过组合算法对特征点进行初匹配;其次,通过随机抽样一致算法剔除误匹配获得目标的精确匹配点,并将精确匹配点通过区域生长算法,经多次迭代实现稠密匹配;最后,经过二次剔除误匹配点得到精确稠密匹配结果.同时,搜索窗的大小通过局部二值模式来自适应调整,提高区域生长后新增种子点的精度,从而更有利于三维重建.实验证明该方法能提高匹配效率和精度.     

An optimal distributed routing algorithm

A distributed optimal one-level routing algorithm is presented. The algorithm is based on Newton's method. Using the variable reduction method, the Hessian matrix becomes diagonal. An example shows that the algorithm has a much faster convergence rate, more accurate results, and better transient behavior than previous work. The algorithm is shown to be convergent, stable, robust, and loop free     

A hybrid association rule mining approach for characterizing network traffic behaviour

Understanding network traffic behaviour is crucial for managing and securing computer networks. One important technique is to mine frequent patterns or association rules from analysed traffic data. On the one hand, association rule mining usually generates a huge number of patterns and rules, many of them meaningless or user‐unwanted; on the other hand, association rule mining can miss some necessary knowledge if it does not consider the hierarchy relationships in the network traffic data. Aiming to address such issues, this paper proposes a hybrid association rule mining method for characterizing network traffic behaviour. Rather than frequent patterns, the proposed method generates non‐similar closed frequent patterns from network traffic data, which can significantly reduce the number of patterns. This method also proposes to derive new attributes from the original data to discover novel knowledge according to hierarchy relationships in network traffic data and user interests. Experiments performed on real network traffic data show that the proposed method is promising and can be used in real applications. Copyright © 2013 John Wiley & Sons, Ltd.     

分布式传感器网络中一种新的模糊航迹关联算法

为了有效提高分布式传感器网络中航迹与航迹关联的计算速度,本文提出了一种新的基于临时航迹和信源相对可信度的多源模糊航迹关联算法。该算法首先在全局融合中心利用来自同一局部融合节点的同一航迹的两个量测形成临时航迹,再由临时航迹与系统航迹融合生成系统航迹,航迹关联是在临时航迹与系统航迹间进行的;并引入信源相对可信度,当有多条临时航迹与系统航迹关联时,选取信源相对可信度最大的临时航迹与系统航迹关联。将该算法用于一个多源航迹关联的仿真实验中,仿真结果表明该算法在保证关联正确率的前提下,与传统的模糊航迹关联算法相比,进一步提高了航迹关联的计算速度和系统航迹的精度,是一种有效的多源航迹关联方法。      

改进的分布式扩散符号LMS算法

针对无线传感器网络(WSN)中数据运算量大及传输速率受限问题,提出了在分布式扩散LMS算法中先后采用符号函数和改进的符号函数量化策略,实现对误差和估计信号的量化,以减少多节点无线传感器网络的运算量和通信量。仿真结果表明,采用符号函数的量化策略时,误差相对较大;采用改进的符号函数量化策略,在设定适当的量化门限时,所提出的算法性能十分接近非量化的扩散LMS算法,且优于非协作算法。特别对于BPSK信号,相较于传统的扩散LMS算法,性能更为优良。     

An extended least-hop distributed routing algorithm

A routing strategy called NELHNET has been developed for networks with multiprecedence traffic and operating under dynamic traffic and topological conditions. An adaptive distributed algorithm that uses least-hop and least-hop-plus-1 routes in a table of routing vectors, as opposed to the usual table of routing scalars, is described. Current delays are passed backward and forward with the packets to allow development of expected delays to each node via all acceptable routes. The route then selected is the acceptable route with the least expected delay. For speedier recovery, a node returning to service receives the current network status from an adjoining node as soon as the link connecting them is operational. The resultant algorithms show far greater than the marginal improvements originally expected over Arpanet simulations. NELHENET strategies also permit the network to function stably under more heavily loaded conditions than do the Arpanet strategies     

Characterizing network traffic behaviour using granule‐based association rule mining

Association rule mining is one important technique to characterize the behaviour of network traffic. However, mining association rules from network traffic data still have three obstacles such as efficiency, huge number of results and insufficiency to represent the behaviour of network traffic. Aiming to tackle these issues, this paper presents a granule‐based association rule mining approach, called association hierarchy mining. The proposed approach adopts top‐down rule mining strategy to directly generate interesting rules according to subjectively specified rule template hierarchies, which improves the efficiency of rule generation and subjectively filters user uninterested rules. The approach also proposes to prune a new type of redundant rules defined by this research to reduce the number of rules. Finally, the approach introduces the concept of diversity, aiming to select the interesting rules for better interpreting the behaviour of network traffic. The experiments performed on the MAWI network traffic traces show the efficiency and effectiveness of the proposed approach. Copyright © 2016 John Wiley & Sons, Ltd.     

An optimized π/2 distributed feedback laser

The effects of a periodicity interruption (phase slip) in distributed feedback lasers are investigated with attention given to the practical aspects of today's semiconductor lasers in the 1.5 μm region. We find that effects on gain threshold and mode discrimination should be large and favorable if the size and placement of a phase slip are correct.     

基于遗传—蚂蚁算法的关联规则挖掘方法

给出一种基于遗传算法和蚂蚁算法相结合的多维多层关联规则挖掘算法,新算法利用了遗传和蚂蚁算法共有的良好全局搜索能力,并克服了经典频集算法的不足,以及遗传算法局部搜索能力弱和蚂蚁算法搜索速度慢的缺陷.实验结果表明,新算法在对具有稀疏特性的多维关联规则的挖掘中体现了良好的性能,提高了生成关联规则的有效性.