基于位对象的最大频繁模式挖掘算法

提出了基于位对象的最大频繁模式挖掘算法．算法中,用位对象表示数据,并用位对象概念改进FP-Tree．用深度优先搜索策略,通过压缩数据库,并用位对象的特性简化模式支持度的计数,使挖掘时不需产生条件FP-Tree和候选项目集,以提高最大频繁模式的挖掘效率．实验结果验证了BFP-Miner的有效性．     

基于图的频繁闭项集挖掘算法

为了提高数据挖掘效率，提出了一种基于图的频繁闭项集挖掘算法GFCG(graph—based frequent closed itemset generation)．该算法采用位矢量技术构造有向图，表示项与项之间的频繁关系，并在有向图的基础上递归产生频繁闭项集，从而只需扫描数据库2次，不产生候选集；引入扩展频繁项集的概念，大大减小了检查频繁项集是否闭的搜索空间．用1个真实数据库和2个合成数据库对GFCG进行了测试，并与A-close和CLOSET算法的结果进行了比较，结果表明，该算法具有良好的速度和可伸缩性性能．     

一个基于Web挖掘的网站优化系统

利用web数据挖掘方法对网站进行优化,提出了网站优化系统的基本框架,并给出相应的web数据挖掘算法:频繁模式发现及序列模式挖掘两个主要算法以找到网站访问者感兴趣的页面集合以及频繁使用的链接序列,该结果以改进网站的设计,提高网站的访问率.     

改进的最大频繁项目序列集挖掘算法

影响关联规则挖掘的关键问题是最大频繁项目序列集的生成问题,而传统的算法往往要求对事务数据库进行多次扫描,从而提高了I/O代价.阐述了项目序列集和它的基本操作的定义,然后详细描述了ISS-DM的最大频繁项目序列集生成算法,并在此基础上提出了一种改进的ISS-DM算法,最后进行了相应的验证.实践证明,改进后的算法同原算法相比,对相同的数据量进行挖掘,算法执行时间明显减少,效率较高.     

基于决策属性的关联规则挖掘

针对具有决策属性的数据库模型,提出了高效挖掘关联规则算法,即矩阵划分算法:根据决策属性将扫描后的数据库划分成两个包含不同决策属性的矩阵,分别采用向量法挖掘频繁项目集.关联规则的生成可充分利用"与"运算的优点,查找规则前件或后件的支持度.所提出的算法减少了候选二项频集的生成,以及"与"运算的大小,与apriori算法及传统的向量法挖掘关联规则相比,效率明显提高.     

基于决策属性的关联规则挖掘

针对具有决策属性的数据库模型,提出了高效挖掘关联规则算法,即矩阵划分算法:根据决策属性将扫描后的数据库划分成两个包含不同决策属性的矩阵,分别采用向量法挖掘频繁项目集.关联规则的生成可充分利用“与”运算的优点,查找规则前件或后件的支持度.所提出的算法减少了候选二项频集的生成,以及“与”运算的大小,与apriori算法及传统的向量法挖掘关联规则相比,效率明显提高.     

基于矩阵的多段支持度关联规则挖掘算法

为有效提高关联规则挖掘算法效率,提出了一种基于矩阵的多段支持度关联规则挖掘算法,该算法通过一次数据库扫描将事务数据存放在矩阵中,利用矩阵进行支持度的计算和频繁集的寻找,同时将项集支持度分段计算的思想应用其中,减少候选集生成,实验表明,算法效率得到了较大提高。     

换道过程中驾驶人感知操作的模式发现与规则挖掘

为发现高速公路下自由换道过程中眼睛感知-手脚操作之间的时序关联性，探索感知与操作相互作用的内在机理，采用驾驶模拟舱进行高速公路驾驶实验，采集眼动数据和车辆运行数据；分别提取换道瞬时和换道全过程的眼睛感知-手脚操作的特征；采用Aprior算法从换道瞬时和换道全过程两个角度发现眼睛感知-手脚操作的频繁模式，挖掘它们的关联规则。对于瞬时感知-操作，左换道发现13种频繁3项集模式，右换道发现18种频繁3项集模式；对于全过程感知- 操作，左换道发现4种频繁模式，右换道发现3种频繁模式。左右换道各自挖掘到6条有实际价值的关联规则。对频繁模式和规则分析发现：右换道比左换道需要较多的感知时间、较复杂的手脚操作行为。发现的频繁模式和挖掘的关联规则描述了自由换道过程中感知操作的特征和它们之间的关联性，能够为安全换道提供参考，为无人驾驶换道操作提供支撑。     

基于分布式算法的FIR数字滤波器的FPGA实现

根据基于查找表结构的分布式算法的基本原理,提出了基于分布式算法的有限脉冲响应数字滤波器(FIR)的实现方法.用FPGA设计并实现了一个32阶低通有限脉冲响应数字滤波器.利用有限脉冲响应数字滤波器线性相位的特性减小了电路规模,采用分割查找表的方法减小了存储空间,采用并行分布式算法结构和流水线技术提高了滤波器的速度.对滤波器性能进行了分析.     

快速空间三角形对相交检测算法

为提高干涉检测的效率,提出了一种快速的三角形对相交检测算法.该算法基于三角形对的各边进行精确相交计算的原理,利用三角形边向量之间的线性相关性、线段的投影特性以及计算的中间结果,提高了三角形对相交检测的效率.采用多种样本对Guigue、Shen、M(o)ller、Tropp等典型算法及本文算法的精度和效率进行测试.结果表明:本文算法与典型算法的检测精度相同,在三角形对样本相交率大于0.2的场合下,本文算法比典型算法的检测速度快3.5％以上,并且检测速度优势随相交率和检测规模的增加而增加.     

A Dynamic Job Scheduling Algorithm for Parallel System

One of the fundamental problems in parallel and distributed systems is deciding how to allocate jobs to processors. The goals of job scheduling in a parallel environment are to minimize the parallel execution time of a job and try to balance the user‘s desire with the system‘s desire. The users always want their jobs be completed as quickly as possible, while the system wants to service as many jobs as possible. In this paper, a dynamic job-scheduling algorithm was introduced. This algorithm tries to utilize the information of a practical system to allo-cate the jobs more evenly. The communication time between the processor and scheduler is overlapped with the computation time of the processor. So the communication overhead can he little. The principle of scheduling the job is based on the desirability of each processor. The scheduler would not allocate a new job to a processor that is already fully utilized. The execution efficiency of the system will he increased. This algorithm also can he reused in other complex algorithms.     

Building Real-Time Network Intrusion Detection System Based on Parallel Time-Series Mining Techniques

A new real-time model based on parallel time-series mining is proposed to improve the accuracy and efficiency of the network intrusion detection systems. In this model, multidimensional dataset is constructed to describe network events, and sliding window updating algorithm is used to maintain network stream. Moreover, parallel frequent patterns and frequent episodes mining algorithms are applied to implement parallel time-series mining engineer which can intelligently generate rules to distinguish intrusions from normal activities. Analysis and study on the basis of DAWNING 3000 indicate that this parallel time-series mining-based model provides a more accurate and efficient way to building real-time NIDS.     

φ-频繁闭项目挖掘问题及其算法

关联规则挖掘问题是数据挖掘领域一个重要的研究方向,φ-关联规则挖掘问题是它的一种推广形式。利用闭项目集的思想,提出了φ-频繁闭项目挖掘问题。它是φ-关联规则挖掘问题的一种替代,并给出了一种有效的挖掘算法,有效解决φ-关联规则挖掘问题中产生大量规则的问题。     

Hybrid Reliability Parameter Selection Method Based on Text Mining,Frequent Pattern Growth Algorithm and Fuzzy Bayesian Network

Reliability parameter selection is very important in the period of equipment project design and demonstration. In this paper, the problem in selecting the reliability parameters and their number is proposed. In order to solve this problem, the thought of text mining is used to extract the feature and curtail feature sets from text data firstly, and frequent pattern tree (FPT) of the text data is constructed to reason frequent item-set between the key factors by frequent patter growth (FPG) algorithm. Then on the basis of fuzzy Bayesian network (FBN) and sample distribution, this paper fuzzifies the key attributes, which forms associated relationship in frequent item-sets and their main parameters, eliminates the subjective influence factors and obtains condition mutual information and maximum weight directed tree among all the attribute variables. Furthermore, the hybrid model is established by reason fuzzy prior probability and contingent probability and concluding parameter learning method. Finally, the example indicates the model is believable and effective.     

经济型自动化船舶电站控制方案研究

文章阐述了为达到经济性目的，在现行四机组船舶电站控制技术上提出了一种新型控制网络解决方案。该控制网络由一主站S7．200PLC、一从站S7—200PLC及四从站PPU（发电机同步保护装置）组成，PLC和PPU分别主要控制电站逻辑实现和发电机运行，主站PLC与从站PLC、PPU间的通信分别通过PPI和MODBUS实现。网络通信和控制逻辑均在STEP7-Micro／WIN中编程实现。应用实践证明其经济性提高的同时性能依然卓越。     

Mining φ-Frequent Itemset Using FP-Tree

The problem of association rule mining has gained considerableprominence in the data mining community for its use as an important tool of knowledge discovery from large-scale databases. And there has been a spurt of research activities around this problem. However, traditional association rule mining may often derive many rules in which people are uninterested. This paper reports a generalization of association rule mining called φ-association rule mining. It allows people to have different interests on different itemsets that are the need of real application. Also, it can help to derive interesting rules and substantially reduce the amount of rules. An algorithm based on FP-tree for mining φ-frequent itemset is presented. It is shown by experiments that the proposed method is efficient and scalable over large databases.     

一种基于闭项集的无冗余关联规则挖掘方法

针对关联规则挖掘中存在的规则数量过多,难于理解和应用的问题,提出了一种基于闭项集的无冗余关联规则挖掘算法．首先,给出了无冗余关联规则的定义,并基于规则信任度的概念说明了该定义的合理性;其次,在生成子、闭项集和无冗余关联规则的基础上,给出了无冗余最小．最大精确规则基和无冗余最小．最大近似规则基的定义,并讨论了它们的剪枝策略．最后,讨论了生成子的性质及连接策略,并在包含索引的基础上,给出了一种宽度优先的无冗余关联规则挖掘算法．实验结果表明,本文提出的算法不仅可以发现规模较小的无冗余关联规则,提高了挖掘结果的可理解性,而且具有较高的挖掘效率．