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为充分发挥教学质量评价在高校中的作用,研究基于关联规则的建筑学专业教学质量评价方法。首先,构建建筑学专业教学质量评价指标体系;其次,基于关联规则挖掘算法,从众多数据中发现数据之间的关联;最后,通过D-S理论确定指标权重,在众多结果中准确获得最终评价结果。实例分析发现:基于关联规则所建立的教学质量评价模型,模拟软件评分与使用本文方法测试的结果相差在0.001~0.009;使用文中方法进行教学质量评价时,平均耗时只需要54.7 s。由此可以看出,关联规则可以提高建筑学专业教学质量评价的有效性。 相似文献
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挖掘支持度和兴趣度最优的数量关联规则 总被引:4,自引:0,他引:4
讨论了数量关联规则提取过程中的连续属性离散化方法和规则的有趣性问题,给出了数量关联规则的客观兴趣度的度量函数,提出用模板匹配方法挖掘用户感兴趣的规则,以解决数量关联规则有趣性的主观评测,研究了一种挖掘支持度和兴趣度最优的形如(A∈[v1,v2]∧)C1)推出C2(其中A为连续属性,C1、C2为类别属性)的数量关联规则方法,并将该方法应用于股市行情分析,实验结果表明是非常有效的. 相似文献
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目前衡量和生成关联规则的主要准则是考虑支持度和置信度阈值,而在实际应用中仅按此准则来挖掘是不够的, 这主要是因为关联规则的评价标准不合理产生的. 针对关联规则评价指标进行了深入的研究, 分析了“支持度-置信度”架构的局限性, 提出了基于相关性的兴趣度的评价指标PS公式, 根据其数学特性指出了它的优点与不足, 为关联规则评价体系的改进奠定了理论基础. 相似文献
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曹志梅 《计算机工程与应用》2005,41(21):215-218
本文基于粗糙集理论和模糊聚类的方法对图书馆的用户评价数据进行了分析,旨在寻找用户评价指标之间的关联规则,确定用户评价的关键性指标。 相似文献
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针对规则随着时间变化的特点,为规则建立元规则对其支持度和置信度变化趋势的分析和预测模型。通过增加支持度向量和置信度向量这两种规则评价指标,给出了动态关联规则元规则的形式化定义。利用自回归Markov模型对动态关联规则的元规则进行了挖掘,并通过实例证明了该方法的有效性。 相似文献
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关联规则挖掘综述 总被引:10,自引:0,他引:10
王小虎 《计算机工程与应用》2003,39(33):190-193
简要论述了关联规则挖掘的研究情况,给出了关联规则的分类方法,分析和评价了关联规则的一些典型算法,指出了关联规则的兴趣度,最后提出了关联规则研究的发展趋势。 相似文献
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Market basket analysis is one of the typical applications in mining association rules. The valuable information discovered from data mining can be used to support decision making. Generally, support and confidence (objective) measures are used to evaluate the interestingness of association rules. However, in some cases, by using these two measures, the discovered rules may be not profitable and not actionable (not interesting) to enterprises. Therefore, how to discover the patterns by considering both objective measures (e.g. probability) and subjective measures (e.g. profit) is a challenge in data mining, particularly in marketing applications. This paper focuses on pattern evaluation in the process of knowledge discovery by using the concept of profit mining. Data Envelopment Analysis is utilized to calculate the efficiency of discovered association rules with multiple objective and subjective measures. After evaluating the efficiency of association rules, they are categorized into two classes, relatively efficient (interesting) and relatively inefficient (uninteresting). To classify these two classes, Decision Tree (DT)‐based classifier is built by using the attributes of association rules. The DT classifier can be used to find out the characteristics of interesting association rules, and to classify the unknown (new) association rules. 相似文献
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《Expert systems with applications》2008,34(4):1110-1116
In data mining applications, it is important to develop evaluation methods for selecting quality and profitable rules. This paper utilizes a non-parametric approach, Data Envelopment Analysis (DEA), to estimate and rank the efficiency of association rules with multiple criteria. The interestingness of association rules is conventionally measured based on support and confidence. For specific applications, domain knowledge can be further designed as measures to evaluate the discovered rules. For example, in market basket analysis, the product value and cross-selling profit associated with the association rule can serve as essential measures to rule interestingness. In this paper, these domain measures are also included in the rule ranking procedure for selecting valuable rules for implementation. An example of market basket analysis is applied to illustrate the DEA based methodology for measuring the efficiency of association rules with multiple criteria. 相似文献
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In data mining applications, it is important to develop evaluation methods for selecting quality and profitable rules. This paper utilizes a non-parametric approach, Data Envelopment Analysis (DEA), to estimate and rank the efficiency of association rules with multiple criteria. The interestingness of association rules is conventionally measured based on support and confidence. For specific applications, domain knowledge can be further designed as measures to evaluate the discovered rules. For example, in market basket analysis, the product value and cross-selling profit associated with the association rule can serve as essential measures to rule interestingness. In this paper, these domain measures are also included in the rule ranking procedure for selecting valuable rules for implementation. An example of market basket analysis is applied to illustrate the DEA based methodology for measuring the efficiency of association rules with multiple criteria. 相似文献
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安秋生 《计算机工程与应用》2005,41(11):152-154
粒计算(GranularComputing,简称GrC)是一种新的软计算方法。该文利用信息颗粒的位表示(BitRepresenta-tions)来进行信息系统软规则及其度量之间关系的研究。具体地,首先利用软规则对关联规则、决策规则、函数依赖之间的关系进行了分析,然后对关联规则度量、决策规则度量、外延的函数依赖度量的关系进行了研究,并且建立了这些度量的统一模型。 相似文献
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The problem of sharp boundary widely exists in image classification algorithms that use traditional association rules. This problem makes classification more difficult and inaccurate. On the other hand, massive image data will produce a lot of redundant association rules, which greatly decrease the accuracy and efficiency of image classification. To relieve the influence of these two problems, this paper proposes a novel approach integrating fuzzy association rules and decision tree to accomplish the task of automatic image annotation. According to the original features with membership functions, the approach first obtains fuzzy feature vectors, which can describe the ambiguity and vagueness of images. Then fuzzy association rules are generated from fuzzy feature vectors with fuzzy support and fuzzy confidence. Fuzzy association rules can capture correlations between low-level visual features and high-level semantic concepts of images. Finally, to tackle the large size of fuzzy association rules base, we adopt decision tree to reduce the unnecessary rules. As a result, the algorithm complexity is decreased to a large extent. We conduct the experiments on two baseline datasets, i.e. Corel5k and IAPR-TC12. The evaluation measures include precision, recall, F-measure and rule number. The experimental results show that our approach performs better than many state-of-the-art automatic image annotation approaches. 相似文献
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基于综合度量的关联规则挖掘算法 总被引:2,自引:0,他引:2
从确定性,有用性,简洁性和新奇性4个方面对规则进行综合度量,给出了规则长度和兴趣度约束的定义,在传统算法基础上提出了基于综合度量的关联规则挖掘算法,使关联规则的挖掘质量得到提高。 相似文献
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数据质量规则是检测数据库质量的关键。为从关系数据库中自动发现数据质量规则,并以其为依据检测错误数据,研究质量规则表示形式及其评估度量,提出以数据项分组及其可信度为依据的最小质量规则计算准则、挖掘算法以及采用质量规则检测错误数据的思路。该数据质量规则形式借鉴关联规则的可信度评估机制、条件函数依赖的表达能力,统一描述函数依赖、条件函数依赖、关联规则等,具有简洁、客观、全面、检测异常数据准确等特性。与相关研究相比,降低挖掘算法的时间复杂度,提高检错率。用实验证明该方法的有效性和正确性。 相似文献
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Bilal Alatas Erhan Akin 《Soft Computing - A Fusion of Foundations, Methodologies and Applications》2008,12(12):1205-1218
This paper proposes a novel particle swarm optimization algorithm, rough particle swarm optimization algorithm (RPSOA), based
on the notion of rough patterns that use rough values defined with upper and lower intervals that represent a range or set
of values. In this paper, various operators and evaluation measures that can be used in RPSOA have been described and efficiently
utilized in data mining applications, especially in automatic mining of numeric association rules which is a hard problem. 相似文献