查询词典 apriori

In discrete-time linear system, apriori H〓 filtering, aposteriori H〓 filtering, L〓 apriori filtering with an H〓 error bound, L〓 aposteriori filtering with an H〓 error bound, robust H〓 apriori filtering, robust apriori L〓 filtering, and robust aposteriori L〓 filtering to noise inputting matrix uncertainty system are considered.

对离散线性系统，研究了确定性系统的H〓验前滤波和验后滤波、H〓约束下的L〓验前滤波及验后滤波、不确定性系统的H〓验前滤波、鲁棒L〓验前滤波及噪声输入矩阵不确定性情况下的鲁棒L〓验后滤波。

The apriori and aposteriori H〓-optimal filtering question can be solved directly. L〓 Apriori and aposteriori filtering with an H〓 error bound can be solved by semidefinite programming. Robust H〓 apriori filtering and robust apriori L〓 filtering in discrete-time system are subject to a nonconvex constraint.

对这些滤波问题解法的改进同连续系统下的情况基本类似，对确定性系统的H〓验前滤波和验后滤波，本文解法可直接得出最优滤波问题的解；对H〓约束下的L〓验前滤波及验后滤波，其解可归结为一凸优化问题；但不确定性系统H〓验前滤波和鲁棒L〓验前滤波的LMI解法中有了非凸约束，因此要用迭代法求解。

this text briefly introduce association rule.by analyzing the application of classic apriori algorithm, apriori algorithm is found that it has some disadvantages. and then farm_new algorithm and the apriori algorithm of riddling compression those improving the association rule algorithm are proposed.

简要地介绍了关联规则，通过对关联分析的经典算法-apriori算法的分析，发现了经典算法apriori算法的缺陷，给出了改进的关联规则算法farm_new算法和基于筛选压缩的apriori挖掘算。

This paper summarizes the background and advantage of data mining, as well as the significance of data mining in the scientific research management of colleges and universities firstly, and then discusses the theory of data mining, association rules and the ideas of main algorithm, analyzes the classic Apriori algorithm and its existing problems as well as the basic solutions. After that, this paper proposes Multi-Dimensional Apriori algorithm which is designed specially for the mining of this paper; Then describes the structure of scientific research data mining system, defines subject-oriented mining tasks, including: mining the data about research projects, mining the data about papers, mining the data about academic writings. The association mining process is implemented by programing, a number of stimulating association rules are found, interpreted and analyzed.

本文首先综述了数据挖掘的研究背景、意义以及数据挖掘技术在高校科研管理中的应用现状和意义，然后在对数据挖掘相关理论、关联规则思想及主要算法进行讨论，分析经典Apriori算法及其存在的问题、基本解决方案后，提出了适合本文挖掘的多维Apriori算法的设计方案，并应用于本文挖掘中；接着论文介绍了科研数据的关联挖掘系统的结构，确定了面向主题的挖掘任务，包括：科研项目信息的挖掘、论文信息的挖掘、学术专著信息的挖掘等；设计了关联规则的实施过程，并通过程序编码得以实现，获得了多条有启发性的关联规则，并对其进行了解释与分析。

According to different purposes, we choose the Association analysis for the relation of alarms, Apriori algorithm, Eclat algorithm and MTAEM algorithm are compared in this paper, Apriori algorithm and MTAEM algorithm are choosed after the comparasion.

文中讨论和分析了IP网管告警数据的特点，根据告警相关的任务，分析了几种数据挖掘技术在IP网管告警数据挖掘中的适用性。

Association rules mining is a very active field in data mining and there are many algorithms today. In this field, the most classical algorithm is Apriori put forward by Agrawal et. al and it is used widely. The paper analyzes the advantages an disadvantages of Apriori, AprioriTid and AprioriHybrid algorithm in mining association rules. To solve the bottle of AprioriHybrid algorithm, a Support-Matrix method to rapidly verify the 2-frequent itemsets is put forward. To accelerate the speed of verify the k-frequent itemsets, a simple and highly efficient method of minimizing the trade database is given. All these two methods improve the efficiency of AprioriHybrid method.

关联规则挖掘是数据挖掘中一个非常活跃的领域，当前有许多种算法，最经典并被广泛使用的是Agrawal等人提出的Apriori算法及其衍生算法，本文分析了Apriori，AprioriTid和AprioriHybrid算法的优缺点，针对AprioriHybrid算法的瓶颈提出一种使用支持度矩阵对频繁2项集快速验证的方法，并给出一种简单易行，而又高效的逐步缩减交易数据库的方法，加快对候选频繁k项集的验证速度，从而提高了AprioriHybrid算法的效率。

Based on the classical Apriori algorithm, a novel graph mining algorithm, Apriori-Graph, is proposed.

在经典的Apriori算法的基础上，提出了一种图挖掘的新算法Apriori-Graph。

The Apriori is an algorithm about the generation of association rule. Enlightened by the generation of candidate k-itemsets, k subsets of one item will be first find out, and then connected to get subsets of two items, and finally get the subsets of k-1 items. The algorithm can be used to find out the proper subset and to generate the protasis and apodosis of the association rule.

在关联规则--Apriori算法方面，主要是受Apriori算法中生成候选k项集方法的启发，提出首先找出k个只含1项的子集，然后将它们连接生成含2项的子集，直到最后连接成k-1项的子集为止的寻找真子集并生成关联规则前件、后件的算法。

During the study of the subject, I have an all-around understanding about the theory and application of Data Mining through reading bookmaking and papers in this field.Based on the research on algorithms, we realized one example following the workflow of Data Mining, which helps us understand the application of the technology.KEYWORDS: data mining , association rule , apriori algorithm

在课题的研究过程中，我通过阅读大量国内外著作、论文，对数据挖掘技术的理论和应用有了一个较为全面的了解，在理论上对关联规则的挖掘算法进行了深入的研究，对于数据挖掘的应用通过实现具体的实例进行研究，并且对挖掘结果进行评价，从而对数据挖掘的应用步骤有了更加深刻的理解。

In fact most Apriori which has gained association Rules suppose whither time or place is effective for ever, but this can"t solve some impersonal problem in morden life, iradionary association Rules in a general way can answer some question for example, there are 90 percent people buy sugar of who buy creamery ,but they can"t answer such question for example the people who buy creamery, but they cant answer such question for example the people who buy creamery of counter one today but there are 90 percent will buy sugar of counter two, moreover, such problem sometimes is the person of decision-making concerned thing because many things have relation with the factor of time and space, so it need consider the factor of time and space in date.

大多数算法得到的关联规则事实上都假定无论是时间还是地点都是永远有效的，但这并不能彻底解决现实生活中的许多客观问题。传统的关联规则一般可以回答诸如"购买了牛奶的人有90％购买了糖"之类的问题，但是不能回答"今天购买了柜台1中牛奶的人明天有90％购买了柜台2中的糖"之类的问题，而这类问题也往往是决策者在实际中所关心的。

"Yes, now you can give yourself airs," she said, you have got what you wanted.

"对了，您现在高兴了，"她说道，这是您所期待的。

Then the LORD said to me: Rebel Israel is inwardly more just than traitorous Judah.

上主于是对我说：＂失节的以色列比失信的犹大，更显得正义。