Distributed adaptive Kalman filter based on variational Bayesian technique

In this paper, distributed Kalman filter design is studied for linear dynamics with unknown measurement noise variance, which modeled by Wishart distribution. To solve the problem in a multi-agent network, a distributed adaptive Kalman filter is proposed with the help of variational Bayesian, where the posterior distribution of joint state and noise variance is approximated by a free-form distribution. The convergence of the proposed algorithm is proved in two main steps: noise statistics is estimated, where each agent only use its local information in variational Bayesian expectation (VB-E) step, and state is estimated by a consensus algorithm in variational Bayesian maximum (VB-M) step. Finally, a distributed target tracking problem is investigated with simulations for illustration.     

一种分布式计算中的容错选举算法

选举算法被广泛应用于分布式计算中,而且它已经被证明比合意问题更难.在分析了选举问题和合意问题的关系之后,提出了一种新的容错选举算法.该算法是稳定的、通信有效的,并且该算法可以容忍进程和链路的暂时性错误.所提出的算法不仅解决了选举问题,并且也提供了解决合意问题的一条新的途径.     

Communication-efficient failure detection and consensus in omission environments

Failure detectors have been shown to be a very useful mechanism to solve the consensus problem in the crash failure model, for which a number of communication-efficient algorithms have been proposed. In this paper we deal with the definition, implementation and use of communication-efficient failure detectors in the general omission failure model, where processes can fail by crashing and by omitting messages when sending and/or receiving. We first define a new failure detector class for this model in terms of completeness and accuracy properties. Then we propose an algorithm that implements a failure detector of the proposed class in a communication-efficient way, in the sense that only a linear number of links are used to send messages forever. We also explain how the well-known consensus algorithm of Chandra and Toueg can be adapted in order to use the proposed failure detector.     

一种基于加权的高效关联规则挖掘算法的设计与实现

文章研究了在大型事务数据库中挖掘加权关联规则的问题,给出了加权频繁模式树的定义,在挖掘算法MIN-WAL(O)和MINWAL(W)的基础上,提出了一种基于加权频繁模式树的加权关联规则挖掘算法-WFPTA,并做了相应的算法比较,试验结果表明算法WFPTA是有效的。     

Clustering distributed data streams in peer-to-peer environments

This paper describes a technique for clustering homogeneously distributed data in a peer-to-peer environment like sensor networks. The proposed technique is based on the principles of the K-Means algorithm. It works in a localized asynchronous manner by communicating with the neighboring nodes. The paper offers extensive theoretical analysis of the algorithm that bounds the error in the distributed clustering process compared to the centralized approach that requires downloading all the observed data to a single site. Experimental results show that, in contrast to the case when all the data is transmitted to a central location for application of the conventional clustering algorithm, the communication cost (an important consideration in sensor networks which are typically equipped with limited battery power) of the proposed approach is significantly smaller. At the same time, the accuracy of the obtained centroids is high and the number of samples which are incorrectly labeled is also small.     

基于频繁模式树的分布式约束性关联规则挖掘算法研究

在分布式环境中挖掘约束性关联规则是当前研究的热点问题之一。该文在FP-growth算法的基础上,提出了一种新的分布式挖掘约束性关联规则算法DAMICFP。该算法对于解决分布式挖掘约束性关联规则的问题是十分有效的。     

基于k最近邻网络的数据聚类算法

聚类研究在数据挖掘研究领域中占有十分重要的地位。虽然目前已有很多数据聚类算法,但精度仍不够理想。文中提出一个基于结构化相似度的网络聚类算法(SSNCA),试图从网络聚类角度进一步提高数据聚类精度。具体解决方案是,将待聚类的向量数据集转化为k最近邻网络,并用SSNCA对该网络进行聚类。将SSNCA与c-Means、仿射传播进行比较,实验表明文中算法得到的目标函数稍差,但聚类精度要明显高于这两个算法。     

Fuzzy self-organization, inferencing, and rule generation

A connectionist inferencing network, based on the fuzzy version of Kohonen's model already developed by the authors, is proposed. It is capable of handling uncertainty and/or impreciseness in the input representation provided in quantitative, linguistic and/or set forms. The output class membership value of an input pattern is inferred by the trained network. A measure of certainty expressing confidence m the decision is also defined. The model is capable of querying the user for the more important input feature information, if required, in case of partial inputs. Justification for an inferred decision may be produced in rule form, when so desired by the user. The connection weight magnitude of the trained neural network are utilized in every stage of the proposed inferencing procedure. The antecedent and consequent parts of the justificatory rules are provided in natural forms. The effectiveness of the algorithm is tested on the vowel recognition problem and on two sets of artificially generated nonconvex pattern classes     

基于自适应人工免疫网络算法的数据挖掘

基于人工免疫网络(Artificial Immune Network:aiNet),提出了一种自适应的人工免疫网络聚类算法。在该算法中,网络抗体间的抑制阀值、抗体的克隆数目、抗体的选择和再选择数目、抗体的变异大小都随网络进化而自适应变化,使最终网络结构更符合原始数据的内在结构,降低了算法对决策者的先验知识的依赖,也提高了算法的泛化能力,很好地解决了算法与问题的相关性。仿真实验结果表明了该算法的有效性和实用性。     

分布式数据挖掘的隐私保护问题

隐私保护是数据挖掘中一个重要的研究方向,如何在不违反隐私规定的情况下,利用数据挖掘工具发现有意义的知识是一个热点问题。本文介绍了分布式数据挖掘中隐私保护的现状,着重介绍分布式数据挖掘中隐私保护问题和技术。     

分布式数据挖掘的隐私保护问题

隐私保护是数据挖掘中一个重要的研究方向，如何在不违反隐私规定的情况下，利用数据挖掘工具发现有意义的知识是一个热点问题。本文介绍了分布式数据挖掘中隐私保护的现状，着重介绍分布式数据挖掘中隐私保护问题和技术。     

基于MapReduce计算模型的并行关联规则挖掘算法研究综述

关联规则挖掘是最常用、最重要的数据挖掘任务之一,经典的关联规则挖掘算法有Apriori、FP-Growth、Eclat等。随着数据的爆炸式增长,传统的算法已不能适应大数据挖掘的需要,需要分布式、并行的关联规则挖掘算法来解决上述问题。MapReduce是一种流行的分布式并行计算模型,因其使用简单、伸缩性好、自动负载均衡和自动容错等优点,得到了广泛的应用。本文对已有的基于MapReduce计算模型的并行关联规则挖掘算法进行了分类和综述,对其各自的优缺点和适用范围进行了总结,并对下一步的研究进行了展望。     

一种应用ODM的入侵检测原型系统

入侵检测系统是网络安全体系的重要组成部分, 本文针对当前入侵检测系统普遍存在的误报、漏报和缺乏自适应性问题,采用ODM的分类算法中的--决策树分类算法、支持向量机分类算法、朴素贝叶斯算法和二元变量逻辑回归算法等4种重点技术对其实验数据进行模型建立和测试,通过对实验的总结分析,在通用的入侵检测模型的基础上建立了一个以数据库为核心的入侵检测模型,并阐述了模型的构成和工作流程.     

挖掘关联规则算法的优化处理

在挖掘关联规则的执行过程中,早期循环生成最大项目集的过程是很重要的。文中提出基于哈希表的算法,对生成侯选项目集的过程进行了优化,尤其是对生成二维侯选项目集更是有效。由于在早期循环中,生成侯选项目集的势较小,使得能更有效地修剪数据库,从而减小了后期循环的计算代价,同时也减小了I／O请求。     

聚类后的关联规则快速更新算法研究*

关联规则和聚类分析是数据挖掘中重要的研究课题。通过对关联规则挖掘算法Apriori算法进行分析与研究,指出了其在实用中存在的两个主要问题。鉴于此,在分析聚类分析和关联规则两种挖掘算法的基础上,讨论了将这两种独立的挖掘方法集成起来的联合挖掘,使其可以有效地压缩数据规模。给出了聚类后的关联规则快速更新算法描述。实验结果表明,算法性能优良,提高了数据挖掘执行效率。     

一个新的分布式最小连通支配集近似算法

在计算机网络中广泛使用广播来解决一些网络问题,设计有效的广播算法是一项重要的课题。文中提出一种分布地计算网络最小连通支配集的近似算法并给出了它的正确性证明。它只需要网络节点具有局部的网络状态信息,可伸缩性强。通过此算法可以在网络中自动形成一个虚拟骨干网,从而可为网络中的广播和路由操作提供一个有效的通信基础。模拟结果表明,文中提出的算法求得的连通支配集小,能较好地应用于一般网络以及移动自组网络中。     

神经网络算法在CRM中的应用

神经网络算法能通过对网络的训练,使客户关系管理系统中客户行为做出预测,便于企业更好为客户服务,改善客户关系,挽留客户.本文就神经网络算法的CRM应用做了相关的探讨.     

A novel neural network for nonlinear convex programming

In this paper, we present a neural network for solving the nonlinear convex programming problem in real time by means of the projection method. The main idea is to convert the convex programming problem into a variational inequality problem. Then a dynamical system and a convex energy function are constructed for resulting variational inequality problem. It is shown that the proposed neural network is stable in the sense of Lyapunov and can converge to an exact optimal solution of the original problem. Compared with the existing neural networks for solving the nonlinear convex programming problem, the proposed neural network has no Lipschitz condition, no adjustable parameter, and its structure is simple. The validity and transient behavior of the proposed neural network are demonstrated by some simulation results.     

面向异常检测的关联模式快速挖掘算法

随着网络和其它信息技术的广泛应用,网络数据流量急剧增长,但现有网络流量异常监测的准确性与实时性均达不到实际应用的需求,迫切需要对流量数据进行快速、深层次的分析.因此,提出一种快速关联模式挖掘算法,通过提取重要的网络数据特征进行关联挖掘,不仅为流量数据分析判断提供及时准确的参考和借鉴,而且提高了监测准确性和效率.     

Distributed Data Mining in Peer-to-Peer Networks

Peer-to-peer (P2P) networks are gaining popularity in many applications such as file sharing, e-commerce, and social networking, many of which deal with rich, distributed data sources that can benefit from data mining. P2P networks are, in fact,well-suited to distributed data mining (DDM), which deals with the problem of data analysis in environments with distributed data,computing nodes,and users. This article offers an overview of DDM applications and algorithms for P2P environments,focusing particularly on local algorithms that perform data analysis by using computing primitives with limited communication overhead. The authors describe both exact and approximate local P2P data mining algorithms that work in a decentralized and communication-efficient manner.