支持度

加州大学中国数据实验室开展的一项调查显示，中国民众对政府的支持度进一步提升，民众对中央政府的平均信任度从2019年6月的8.23分上升到了去年5月的8.87分（满分为10分）。

A survey conducted by the University of California 's China Data Lab showed support for the government among the Chinese public has risen , with the average level of trust in the central government increasing from 8.23 in June 2019 to 8.87 in May last year , measured on a scale of one to 10 .

多传感器支持度和自适应加权时空融合算法

Support Degree and Adaptive Weighted Spatial-temporal Fusion Algorithm of Multi-sensor

针对文本关联分析中难以确定最小支持度阈值的问题，提出N个最频繁项集挖掘算法。

Research on mining the top N most frequent item sets in text collection .

在进化树上，有一个功能未知的家族（UNKNOWN）被发现并有很高的支持度。

An unknown family with high support is found in the evolutionary tree .

研究D-S证据理论及其改进算法，引入聚类分析中的欧式距离度量各证据之间的支持度，据此确定证据的权值，使用权值对证据理论进行修正；

Third , D-S evidence theory and its improved algorithms are studied ;

从最终的试验结果可以看出，ERIC算法在较小支持度的情况下对中大型数据库有很好的搜索效率。

From the experimental results , ERIC has a perfect efficiency in the cases of large datasets and low support threshold .

给定一个不确定图G和支持度阈值minsup，近邻模式挖掘就是找出G中所有支持度超过minsup的标签集合。

Given an uncertain graph G and a support threshold min_sup , proximity pattern mining on G is to find all the label sets whose support exceed min_sup .

利用云模型的理论与方法求解数量关联问题，给出了一种云关联规则的定义，并提出了基于云模型理论支持度和置信度的计算方法，最后提出了一种提取算法CloudModelA。

The article proposed an approach for mining quantitative association rules . It gave the definition of the cloud model association rules and presented an algorithm of support and confidence based on cloud model , at last , presented an efficient algorithm Cloud Model A.

其次介绍了传统的基于支持度可信度框架的关联规则挖掘算法Apriori算法；最后提出并详细论述了一种交互式关联规则挖掘算法IMAR算法。

Then is a traditional algorithm ( Apriori ) of association rule mining based on support-confidence framework .

利用置信度、支持度和LS充分性因子等评价指标对学习结果进行取舍。

The induction path of each node in the concept tree is recorded as node aggregate . The learning results are evaluated with support , confidence and LS sufficient factor .

eEPMiner采用模式增长的策略，只需两次扫描事务数据库，就能挖掘出C类上所有的eEPs，并同时得到它们的增长率和支持度。

The algorithm uses the pattern fragment growth strategy , and only needs to scan dataset twice to get eEPs with their support and growthrate .

对于加权后的Apriori算法中支持度和置信度的运算不再适用的情况，给出基于利润加权关联规则中的加权支持度和加权置信度的定义。

For the support and confidence of weighted Apriori algorithm is no longer applicable , it gives the definition of support and confidence which base on weight .

面向目标的基于效用度的关联规则挖掘（OOA挖掘）是在给定目标的情况下，挖掘满足支持度、置信度和效用度阈值的规则。

The OOA mining hopes to mine all association rules based on a given objective , support , confidence and utility .

传统的Apriori算法由于始终保持单一的最小支持度，所以在实际应用中不能挖掘小比例事件中的关联规则。

Because the traditional Apriori algorithm always keeps a single mini-support , it can not bring out in the practical application the association rules from the little probability items .

将该可信度体现为加权D-S证据理论组合规则中的证据权值，综合考虑传感器支持度及其与目标距离，给出了权值确定方法。

As the decision credibility can be presented by the corresponding weight of the weighted D-S evidence theory , it was obtained by a formula based on the two elements .

简单介绍了智能信息处理中的RoughSet（RS）理论，引入支持度作为条件属性和决策属性之间的度量，同时介绍了支持度的计算方法。

Theory of Rough Set in intelligent data process is introduced in brief , the support degree is introduced as measurement between conditional attribute and decision-making attribute , and at the same time , a method for calculating the support degree is introduced , too .

该文在FP树的基础上，引入支持度函数的概念，对FP树进行改造，提出了一种关于挖掘关联规则的增量更新算法IFPgrowth。

Firstly the concept of support function is introduced . Then an incremental updating model is presented , which contains the construction of IFP-tree and IFP-tree growth algorithm .

其基本评价步骤为：获取证据源数据，确定评价标准和基本支持度；运用模糊层次分析法（FAHP）确定各指标权重；构造mass函数；进行风险综合评价。

The steps are : Obtain source data of evidence ; determine the evaluation criteria and the basic support ; determine the index weight by FAHP ; structure mass function ; do comprehensive evaluation of risk .

为了对超级市场的购物篮进行分析，R.Agrawal等人于1993年首先开创性的提出了关联规则（AssociationRules）挖掘理论，并建立了支持度-可信度框架。

In order to make an analysis on shopping basket of supermarket , R. Agrawal , et al . put forward the Association Rules of mining theory and established the framework of Support - credibility in 1993 .

FP-tree算法是对类Apriori算法的一次革命，该算法只需要扫描两次数据库，但由于采用的是统一的支持度，也使该算法丧失一些优势。

At the same time , Apriori-based Algorithm produces a large number of candidate sets . FP-tree Algorithm is a revolution of Apriori-based Algorithm , because it only need scan database two times .

Apriori算法是采用支持度-置信度模型算法，但是Apriori算法存在一些缺陷，包括多次扫描数据库、产生大量无用规则以及无法处理增量数据集。

The Apriori algorithm is an algorithm that uses the support-confidence model , but it has some shortcomings , including repeatedly scanning the database , generating a lot of useless rules , and cannot handle incremental data sets .

MDRBR算法通过规则支持度进行约束，可有效提高缺省规则的挖掘效率。

MDRBR algorithm improves efficiency of existing algorithms for mining default rules with support measure constraint .

为了提高E-FP算法的效率，我们在挖掘过程中采用了可变支持度阀值。

We call it E-FP . To increase the efficiency , we push various support constraints into the mining process .

针对支持度置信度框架的GSP算法的产生的序列模式很多时候不是用户感兴趣的，有时甚至会产生误导这一问题，我们提出用统计学中的χ2测试来衡量序列模式的相关性。

Motivated by the goal of solving the problem-the support-confidence framework does not work well when correlation is the appropriate measure , we propose measuring significance of sequential patterns via the χ ~ 2 test for correlation from classical statistics .

最后，针对Apriori算法在案件分析中存在的不足，即忽略案件属性重要程度的差异，提出加权的Apriori算法，在计算支持度时引入权值的概念，通过计算信息增益来确定权值。

At last , for the shortage of the Apriori algorithm in case analysis , which ignores the crucial differences of case property , this paper presents an Apriori algorithm based on weight , which identifies the weight by calculating the information gain .

结果主要影响因素为SCL-90的强迫状态、人际敏感、抑郁和躯体化等因素，积极的应对方式，社会支持度，以及对抗击SARS工作的认知评价。

Results The main relative factors of mental health of the medical staff were obsessive-compulsive , interpersonal sensitivity , depression , somatization of SCL-90 , positive coping style , social support , the evaluation of work , the total appraisal of living on SARS and so on .

仿真结果表明基于Apriori的类别关联规则的挖掘算法能够挖掘出用户的个性化信息，同时表明用户个性化信息的质量好坏与支持度值的大小密切相关。

Simulation results indicates that the mining algorithm based on the association rule ( s ) of Apriori ( algorithm ) can dig out customers ' personalized information , and the quality of the personalized information has close relationship with the degree of supporting .

提出了Apriori-2算法，新算法在计算候选大项集支持度所涉及的记录数目将小于事务数据库中原始的记录数目，提高了原算法的效率，具有一定的实用性。

The new Apriori-2 algorithm has the advantage of less record numbers compared with work DB , high efficiency and certain practical significance .

结果：单因素分析提示，婚姻、受教育程度、生活质量（心理、社会、物质功能维度）、社会支持度在MCI和正常老年人组之间的差异有统计学意义（p0.05）。

Results : But the factor analysis the level of education pointing out marriage , accepting , the quality of life ( mentality , society , matter function preserve degree ), society support degree between MCI and regular old people group difference has statistics meaning ( p 0.05 ) .

之前的许多研究都是采用Apriori类的候选生成-检验方法或基于FP-Tree的方法，而产生大量候选和动态创建大量FP-Tree的代价太高，特别是在支持度阈值较小或存在长模式时。

Most of the previous studies adopt an Apriori-like candidate set generation-and-test approach or based on FP-Tree . However , candidate set generation and creating conditional FP-Tree dynamically are very costly , especially when there exists low threshold of support or long patterns .