查询词典 apriori

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。

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。

The proposed algorithms for mining multi-level association rules such as Cumulate algorithm, ML_T2L1 algorithm are based on Apriori algorithm.

已有的多层关联规则挖掘算法如Cumulate 算法、ML-T2L1 算法，都是通过对Apriori 算法进行扩展得到的。

Aiming at the multiple loop structures in quality management process network, an improved graph pattern mining approach based on Apriori algorithm is put forward, where a frequent pattern of size k, denoted as G, is joined with another frequent pattern of size k, denoted as G, to form a candidate frequent pattern of size (k+1), if there exists a source vertex s in G and a sink vertex e in G such that the graph derived from G by subtracting node s equals with the graph derived from G G by subtracting node e.

采用基于APriori方法的频繁活动序列挖掘算法，认为k-频繁图集中，当一个图减去其中的一个源顶点后，如果所得到的图与另一个图减去其中的一个沉顶点后的图相同时，可以连接生成一个(k+1)-候选频繁图，从而减少了传统Apriori算法迭代过程中生成的冗余候选频繁图的数目。

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。

The second plan realizes the idea of applying the method that generates candidate k-itemsets from the Apriori arithmetic in the finding of proper subset, which helps to generate a comparatively detailed association rule, and can be a good reference for the Call Center service quality management.

通过第二种方案实现了将Apriori算法中生成候选k项集的方法运用于寻找真子集的思想，挖掘出了较为详细的关联规则，为呼叫中心服务质量管理提供了很好的参考。

The improvements were renumbering the frequent 1 item in the support degree descending order and utilizing the Apriori property to prune the redundant extended items which were used to generate the candidate frequent itemsets.

改进主要体现在两个方面：按支持度降序对频繁1项重新编号再构建关联图；利用Apriori性质删减用来生成候选项集的冗余扩展项节点。

Based on the empirical result, we compare the Apriori algorithm with this improved algorithm.

在实验的基础上对Apriori算法和改进的算法进行了比较，实验结果表明，在特定的数据库中，改进的算法在挖掘效率上优于 Apriori算法。

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。

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。

The absorption and distribution of chromium were studied in ryeusing nutrient culture technique and pot experiment.

采用不同浓度K2CrO4(0，0.4，0.8和1.2 mmol/L)的Hoagland营养液处理黑麦幼苗，测定铬在黑麦体内的亚细胞分布、铬化学形态及不同部位的积累。

By analyzing theory foundation of mathematical morphology in the digital image processing, researching morphology arithmetic of the binary Image, discussing two basic forms for the least structure element: dilation and erosion.

通过分析数学形态学在图像中的理论基础，研究二值图像的形态分析算法，探讨最小结构元素的两种基本形态：膨胀和腐蚀；分析了数学形态学复杂算法的基本原理，把数学形态学的部分并行处理理念引入到家实际应用中。