基于时间衰减模型的模糊会话关联规则挖掘算法

现有的关联规则挖掘算法没有考虑数据流中会话的非均匀分布特性和历史数据的作用,并且忽略了连续属性处理时的“尖锐边界”问题。针对这些问题,本文提出一种基于时间衰减模型的模糊会话关联规则挖掘算法。首先,针对数据流中会话的非均匀分布特性,基于时间片对会话进行划分,完整的保留了时间片内会话之间的相关性信息;然后,采用模糊集对会话的连续属性进行处理,增加了规则的兴趣度和可理解性;最后,在考虑历史数据作用和允许误差情况的基础上,基于时间衰减模型挖掘数据流中的临界频繁项集和模糊关联规则。实验结果表明,本文方法在提高时间效率、降低冗余率和增加规则兴趣度方面存在明显优势。     

Mining multiple-level association rules in large databases

A top-down progressive deepening method is developed for efficient mining of multiple-level association rules from large transaction databases based on the a priori principle. A group of variant algorithms is proposed based on the ways of sharing intermediate results, with the relative performance tested and analyzed. The enforcement of different interestingness measurements to find more interesting rules, and the relaxation of rule conditions for finding “level-crossing” association rules, are also investigated. The study shows that efficient algorithms can be developed from large databases for the discovery of interesting and strong multiple-level association rules     

Ranking discovered rules from data mining with multiple criteria by data envelopment analysis

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.     

Mining fuzzy specific rare itemsets for education data

Association rule mining is an important data analysis method for the discovery of associations within data. There have been many studies focused on finding fuzzy association rules from transaction databases. Unfortunately, in the real world, one may have available relatively infrequent data, as well as frequent data. From infrequent data, we can find a set of rare itemsets that will be useful for teachers to find out which students need extra help in learning. While the previous association rules discovery techniques are able to discover some rules based on frequency, this is insufficient to determine the importance of a rule composed of frequency-based data items. To remedy this problem, we develop a new algorithm based on the Apriori approach to mine fuzzy specific rare itemsets from quantitative data. Finally, fuzzy association rules can be generated from these fuzzy specific rare itemsets. The patterns are useful to discover learning problems. Experimental results show that the proposed approach is able to discover interesting and valuable patterns from the survey data.     

影响关联规则挖掘的有趣性因素的研究

关联规则挖掘是数据挖掘研究中的一个重要方面,而其中一个重要问题是对挖掘出的规则的感兴趣程度的评估。实际应用中可从数据源中挖掘出大量的规则,但这些规则中的大部分对用户来说是不一定感兴趣的。关联规则挖掘中的有趣性问题可从客观和主观两个方面对关联规则的兴趣度进行评测。利用模板将用户感兴趣的规则和不感兴趣的规则区分开,以此来完成关联规则有趣性的主观评测;在关联规则的置信度和支持度基础上对关联规则的有趣性的客观评测增加了约束。     

Construction and evaluation of structured association map for visual exploration of association rules

The association rule mining is one of the most popular data mining techniques, however, the users often experience difficulties in interpreting and exploiting the association rules extracted from large transaction data with high dimensionality. The primary reasons for such difficulties are two-folds. Firstly, too many association rules can be produced by the conventional association rule mining algorithms, and secondly, some association rules can be partly overlapped. This problem can be addressed if the user can select the relevant items to be used in association rule mining, however, there are often quite complex relations among the items in large transaction data. In this context, this paper aims to propose a novel visual exploration tool, structured association map (SAM), which enables the users to find the group of the relevant items in a visual way. The appearance of SAM is similar with the well-known cluster heat map, however, the items in SAM are sorted in more intelligent way so that the users can easily find the interesting area formed by a set of associated items, which are likely to constitute interesting many-to-many association rules. Moreover, this paper introduces an index called S2C, designed to evaluate the quality of SAM, and explains the SAM based association analysis procedure in a comprehensive manner. For illustration, this procedure is applied to a mass health examination result data set, and the experiment results demonstrate that SAM with high S2C value helps to reduce the complexities of association analysis significantly and it enables to focus on the specific region of the search space of association rule mining while avoiding the irrelevant association rules.     

基于最小兴趣度的正、负关联规则挖掘

根据Piatetsky-Shapiro的主张,只有满足最小兴趣度条件约束的规则才是有兴趣的,论文将这一条件推广到了负关联规则,并将该条件进行了改进,使其能够对关联规则的相关性进行判断,然后在此基础上提出一个能同时挖掘正负关联规则的算法,实验表明该算法是非常有效的。     

由规则归纳系统中发掘感兴趣模式

文章借助粗糙集理论，提出一种面向大型数据库的规则归纳方法；然后在评价规则的置信度和支持度的基础上，引入兴趣度准则进一步对规则进行评价，并介绍了两种针对不同问题的兴趣度指标，提出兴趣模板的概念来描述令人感兴趣的规则特征以强化用户的参与作用，提高系统效率。     

Product portfolio identification with data mining based on multi-objective GA

In the initial stage of product design, it is essential to define product specifications according to various market niches. An important issue in this process is to provide designers with sufficient design knowledge to find out what customers really want. This paper proposes a data mining method to facilitate this task. The method focuses on mining association rules that reflect the mapping relationship between customer needs and product specifications. Four objectives, support, confidence, interestingness and comprehensibility, are used for evaluating the extracted rules. To solve such a multi-objective problem, a Pareto-based GA is utilized to perform the rule extraction. Through computational experiments on an electrical bicycle case, it is shown that our approach is capable of extracting useful and interesting knowledge from a design database.     

空间关联规则的双向挖掘

空间数据库中关联规则挖掘不仅需要考虑关系元组属性之间的关系——纵向关系,更需要挖掘元组之间的关系——横向关系,如相邻、相交、重叠等。本文通过分析空间数据库的存储模式,借鉴事务数据库关联规则的挖掘方法,对空间关联规则进行完整定义,并对规则的兴趣度度量进行探讨。根据挖掘的方向将空间数据挖掘归纳为纵向挖掘、横向挖掘、双向挖掘。在双向挖掘中,提出一种新算法,该算法根据挖掘任务进行约束,缩小挖掘空间,然后通过空间计算将空间关系转化为非空间关系,经过多次循环,获取非空间项集,进而挖掘出空间关联规则。据此提出空间数据双向挖掘工作流程,并通过实例进行了验证。     

Ranking discovered rules from data mining with multiple criteria by data envelopment analysis

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.     

正负关联规则两级置信度阈值设置方法

针对传统正负关联规则置信度阈值设置方法难以控制低可信度规则数量和易遗漏有趣规则的问题,提出了一个结合项集相关性的两级置信度阈值设置方法（PNMC-TWO）。首先,基于规则的无矛盾性、有效性和有趣性考虑,以相关度-支持度-置信度为框架,从规则置信度与项集支持度的计算关系出发,系统地分析了正负关联规则置信度取值随规则的项集支持度大小变化的规律;然后,与实际挖掘中用户对高可信度且有趣的规则需求相结合,提出了一个新的设置模型,避免了传统方法设置阈值时的盲目性和随意性;最后,从规则数量和规则质量两方面对所提方法与原双阈值法进行了实验对比。实验结果表明,所提方法不仅可以更好地确保提取出的关联规则有效和有趣,还可以显著地降低可信度低的关联规则数量。     

关联规则挖掘在市场监管工作中的应用

关联规则是为了挖掘出隐藏在数据中的相互关系,找出所有能把一组事件或数据项与另一组事件或数据项联系起来的规则,从而辅助决策者进行决策。结合市场监督管理部门监管数据的实际情况,抽取市场主体部分基本信息和监管部门录入的违规、违法数据生成违规违法事务数据库,再将事务数据库转换为布尔矩阵,采用基于向量内积的关联规则挖掘方法生成频繁项集,进行关联规则挖掘。实验结果表明,该方法能够快速、准确地挖掘出相应的关联规则,符合市场监管部门日常工作的实际情况,对实际工作具有一定的指导意义。     

基于关联规则的决策树算法

通过将关联规则与决策树算法相结合,形成一种基于关联规则的决策树算法。该算法对不同时期同一事务的异种数据结构进行处理,得到一种可扩展的多分支分类决策树,使得改进后的决策树算法具有良好的可扩展性。该算法解决了传统分类算法在数据集维度发生变化时分类过程无法持续进行的问题。     

Mining association rules from quantitative data

Data-mining is the process of extracting desirable knowledge or interesting patterns from existing databases for specific purposes. Most conventional data-mining algorithms identify the relationships among transactions using binary values, however, transactions with quantitative values are commonly seen in real-world applications. This paper thus proposes a new data-mining algorithm for extracting interesting knowledge from transactions stored as quantitative values. The proposed algorithm integrates fuzzy set concepts and the apriori mining algorithm to find interesting fuzzy association rules in given transaction data sets. Experiments with student grades at I-Shou University were also made to verify the performance of the proposed algorithm.     

挖掘所关注规则的多策略方法研究

通过数据挖掘,从大型数据库中发现了大量规则,如何选取所关注的规则,是知识发现的重要研究内容。该文研究了利用领域知识对规则的主观关注程度进行度量的方法,给出了一个能够度量规则的简洁性和新奇性的客观关注程度的计算函数,提出了选取用户关注的规则的多策略方法。     

基于规则兴趣度的关联分类

关联分类具有较高的分类精度和较强的适应性,然而由于分类器是由一组高置信度的规则构成,有时会存在过度拟合问题。提出了基于规则兴趣度的关联分类（ACIR）。它扩展了TD-FP-growth算法,使之有效地挖掘训练集,产生满足最小支持度和最小置信度的有趣的规则。通过剪枝选择一个小规则集构造分类器。在规则剪枝过程中,采用规则兴趣度来评价规则的质量,综合考虑规则的预测精度和规则中项的兴趣度。实验结果表明该方法在分类精度上优于See5、CBA和CMAR,并且具有较好的可理解性和扩展性。     

Interestingness measures for association rules based on statistical validity

Assessing rules with interestingness measures is the pillar of successful application of association rules discovery. However, association rules discovered are normally large in number, some of which are not considered as interesting or significant for the application at hand. In this paper, we present a systematic approach to ascertain the discovered rules, and provide a precise statistical approach supporting this framework. The proposed strategy combines data mining and statistical measurement techniques, including redundancy analysis, sampling and multivariate statistical analysis, to discard the non- significant rules. Moreover, we consider real world datasets which are characterized by the uniform and non-uniform data/items distribution with a mixture of measurement levels throughout the data/items. The proposed unified framework is applied on these datasets to demonstrate its effectiveness in discarding many of the redundant or non-significant rules, while still preserving the high accuracy of the rule set as a whole.     

Mining Informative Rule Set for Prediction

Mining transaction databases for association rules usually generates a large number of rules, most of which are unnecessary when used for subsequent prediction. In this paper we define a rule set for a given transaction database that is much smaller than the association rule set but makes the same predictions as the association rule set by the confidence priority. We call this rule set informative rule set. The informative rule set is not constrained to particular target items; and it is smaller than the non-redundant association rule set. We characterise relationships between the informative rule set and non-redundant association rule set. We present an algorithm to directly generate the informative rule set without generating all frequent itemsets first that accesses the database less frequently than other direct methods. We show experimentally that the informative rule set is much smaller and can be generated more efficiently than both the association rule set and non-redundant association rule set.