挖掘语言值关联规则

讨论了大型数据库上数量属性的关联规则问题.为了软化论域的划分边界,应用相关的模糊c-方法(relationalfuzzyc-means,简称RFCM)算法确定正态模糊数的两个参数,并借助正态模糊数模型来划分数量属性的论域,由此生成一系列的语言值关联规则.另外,给出了语言值关联规则的挖掘方法.由于语言值能很好地表示抽象的概念,从而使得挖掘出的关联规则更抽象、更容易被人理解.     

加权关联规则挖掘算法的研究

讨论了加权关联规则的挖掘算法，对布尔型属性，在挖掘算法MINWAL（O）和MINWAL（W）的基础上给出一种改进的加权关联规则挖掘算法，此算法能有效地考虑布尔型属必的重要性和规则中所含属性的个数，对数量型属性，应用竞争聚集算法将数量型属性划分成若干个模糊集，产系统地提出加权模糊关联规则的挖掘算法，此算法能有效地考虑数量型属性的重要性和规则中所含属性的个数，并适用于大型数据库。     

Deriving support threshold values and membership functions using the multiple-level cluster-based master–slave IFG approach

Today, development of e-commerce has provided many transaction databases with useful information for investigators exploring dependencies among the items. In data mining, the dependencies among different items can be shown using an association rule. The new fuzzy-genetic (FG) approach is designed to mine fuzzy association rules from a quantitative transaction database. Three important advantages are associated with using the FG approach: (1) the association rules can be extracted from the transaction database with a quantitative value; (2) extracting proper membership functions and support threshold values with the genetic algorithm will exert a positive effect on the mining process results; (3) expressing the association rules in a fuzzy representation is more understandable for humans. In this paper, we design a comprehensive and fast algorithm that mines level-crossing fuzzy association rules on multiple concept levels with learning support threshold values and membership functions using the cluster-based master–slave integrated FG approach. Mining the fuzzy association rules on multiple concept levels helps find more important, useful, accurate, and practical information.     

基于RPCL的模糊关联规则挖掘

在模糊关联规则的挖掘过程中,很难预先知道每个属性合适的模糊集。针对该问题,提出基于次胜者受罚竞争学习的模糊关联规则挖掘算法,无需先验知识,即可根据每个属性的性质找出对应的模糊集,并确定模糊集的数目。实验结果表明,与同类算法相比,该算法可以挖掘出更多有趣的关联规则。     

一种模糊多层关联规则挖掘算法

针对传统数据挖掘中的“尖锐边界”问题,采用将模糊理论和关联规则挖掘技术相结合的思想,在改进传统Apriori算法的基础上,结合多层关联规则挖掘的方法,提出了一种模糊多层关联规则挖掘算法。对模糊多层关联规则挖掘的基本概念进行了定义,详细描述了模糊多层关联规则挖掘算法。最后用Visual FoxPro6．0语言实现了该算法程序,通过交易数据库挖掘实验表明算法是有效的。     

一种改进的模糊关联算法及其在IDS中的应用

关联规则的发现是数据挖掘中的一个重要问题,但只是对离散型数据进行处理。为解决连续数量值属性的划分出现的“尖锐边界”问题,采用模糊划分,实现数据平滑过渡。由于入侵检测系统(IDS)对训练数据要求不高,文中提出了一种使用哈希链表改进模糊关联规则挖掘的新算法,且在挖掘过程中使用了等价类快速查找频繁项集,避免了反复扫描数据库及大量重复计算检验步骤。通过一个入侵检测系统的算例显示了其优越性,来提高对入侵数据的识别能力。     

一种改进的量化关联规则算法在零售业中的应用

以超市的量化属性为研究对象,提出一种基于模糊聚类和减类聚类的量化关联规则算法.该算法基本思想是把模糊聚类技术融入到离散化过程中,使数据离散到合理的区间,再利用经典的布尔关联规则挖掘算法Apriori进行挖掘.实验证明,这种方法能够有效挖掘量化关联规则,提高交叉销售的可能性.     

Applying cluster-based fuzzy association rules mining framework into EC environment

During electronic commerce (EC) environment, how to effectively mine the useful transaction information will be an important issue to be addressed in designing the marketing strategy for most enterprises. Especially, the relationships between different databases (e.g., the transaction and online browsing database) may have the unknown and potential knowledge of business intelligence. Two important issues of mining association rules were mentioned to address EC application in this study. The first issue is the discovery of generalized fuzzy association rules in the transaction database. The second issue is to discover association rules from the web usage data and the large itemsets identified in the transaction database. A cluster-based fuzzy association rules (CBFAR) mining architecture is then proposed to simultaneously address such two issues in this study. Three contributions were achieved as: (a) an efficient fuzzy association rule miner based on cluster-based fuzzy-sets tables is presented to identify all the large fuzzy itemsets; (b) this approach requires less contrast to generate large itemsets; (3) a fuzzy rule mining approach is used to compute the confidence values for discovering the relationships between transaction database and browsing information database. Finally, a simulated example during EC environment is provided to demonstrate the rationality and feasibility of the proposed approach.     

A novel unsupervised fuzzy clustering method for preprocessing of quantitative attributes in association rule mining

In Association rule mining, the quantitative attribute values are converted into Boolean values using fixed intervals. Conventional association rule mining algorithms are then applied to find relations among the attribute values. These intervals may not be concise and meaningful enough for human users to easily obtain non trivial knowledge from those rules discovered. Clustering techniques can be used for segmenting quantitative values into meaningful groups instead of fixed intervals. But the conventional clustering techniques like k-means and c-means require the user to specify the number of clusters and initial cluster centres. This initialization is one of the major challenges of clustering. A novel fuzzy based unsupervised clustering algorithm proposed by the authors is extended to segment quantitative values into fuzzy clusters in this paper. Membership values of quantitative items in the partitioning fuzzy clusters are used with weighted fuzzy rule mining techniques to find natural association rules. This fuzzy based method for handling quantitative attributes is compared with that of fixed intervals and segmenting using conventional k-means clustering method along with Apriori algorithm.     

Multi-objective genetic algorithms based automated clustering for fuzzy association rules mining

Researchers realized the importance of integrating fuzziness into association rules mining in databases with binary and quantitative attributes. However, most of the earlier algorithms proposed for fuzzy association rules mining either assume that fuzzy sets are given or employ a clustering algorithm, like CURE, to decide on fuzzy sets; for both cases the number of fuzzy sets is pre-specified. In this paper, we propose an automated method to decide on the number of fuzzy sets and for the autonomous mining of both fuzzy sets and fuzzy association rules. We achieve this by developing an automated clustering method based on multi-objective Genetic Algorithms (GA); the aim of the proposed approach is to automatically cluster values of a quantitative attribute in order to obtain large number of large itemsets in less time. We compare the proposed multi-objective GA based approach with two other approaches, namely: 1) CURE-based approach, which is known as one of the most efficient clustering algorithms; 2) Chien et al. clustering approach, which is an automatic interval partition method based on variation of density. Experimental results on 100 K transactions extracted from the adult data of USA census in year 2000 showed that the proposed automated clustering method exhibits good performance over both CURE-based approach and Chien et al.’s work in terms of runtime, number of large itemsets and number of association rules.     

Mining fuzzy sequential patterns from quantitative transactions

Many researchers in database and machine learning fields are primarily interested in data mining because it offers opportunities to discover useful information and important relevant patterns in large databases. Most previous studies have shown how binary valued transaction data may be handled. Transaction data in real-world applications usually consist of quantitative values, so designing a sophisticated data-mining algorithm able to deal with various types of data presents a challenge to workers in this research field. In the past, we proposed a fuzzy data-mining algorithm to find association rules. Since sequential patterns are also very important for real-world applications, this paper thus focuses on finding fuzzy sequential patterns from quantitative data. A new mining algorithm is proposed, which integrates the fuzzy-set concepts and the AprioriAll algorithm. It first transforms quantitative values in transactions into linguistic terms, then filters them to find sequential patterns by modifying the AprioriAll mining algorithm. Each quantitative item uses only the linguistic term with the maximum cardinality in later mining processes, thus making the number of fuzzy regions to be processed the same as the number of the original items. The patterns mined out thus exhibit the sequential quantitative regularity in databases and can be used to provide some suggestions to appropriate supervisors.     

基于改进多层次模糊关联规则的定量数据挖掘算法

针对单一层次结构实现规则提取具有规则提取准确性不高、算法运行时间长、难以满足用户使用需求的问题,提出一种基于改进多层次模糊关联规则的定量数据挖掘算法。采用高频项目集合,通过不断深化迭代的方法形成自顶向下的挖掘过程,整合模糊集合理论、数据挖掘算法以及多层次分类技术,从事务数据集中寻找模糊关联规则,挖掘出储存在多层次结构事务数据库中定量值信息的隐含知识,实现用户的定制化信息挖掘需求。实验结果表明,提出的数据挖掘算法在挖掘精度和运算时间方面相较于其他算法具有突出优势,可为多层次关联规则提取方法的实际应用带来新的发展空间。     

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.     

大型数据库的模糊关联规则挖掘算法研究

为解决传统关联规则挖掘算法对大规模连续数据库进行挖掘时所产生的信息损失和效率低下等问题,给出一种改进的模糊关联规则挖掘算法,称为F-ARMVLQD算法。该算法利用模糊均值聚类算法解决离散属性间隔之间出现"尖锐边界"的问题,同时算法引入有向无环图和字节向量用以提高频繁项目集的计算效率,并吸取分区算法的优势,解决对该数据库挖掘时磁盘操作频繁的问题,整个算法只需扫描两次数据库。实验结果表明,该算法比传统算法具有更高的执行效率。     

Generalized Affinity-Based Association Rule Mining for Multimedia Database Queries

The recent progress in high-speed communication networks and large-capacity storage devices has led to a tremendous increase in the number of databases and the volume of data in them. This has created a need to discover structural equivalence relationships from the databases since queries tend to access information from structurally equivalent media objects residing in different databases. The more databases there are, the more query-processing performance improvement can be achieved when the structural equivalence relationships are automatically discovered. In response to such a demand, association rule mining has emerged and proven to be a highly successful technique for discovering knowledge from large databases. In this paper, we explore a generalized affinity-based association rule mining approach to discover the quasi-equivalence relationships from a network of databases. The algorithm is implemented and two empirical studies on real databases are conducted. The results show that the proposed generalized affinity-based association rule mining approach not only correctly exploits the set of quasi-equivalent media objects from the databases, but also outperforms the basic association rule mining approach in the discovery of the quasi-equivalent media object pairs. Received 16 September 1999 / Revised 12 September 2000 / Accepted in revised form 9 January 2001     

基于多最小支持度的多层模糊关联规则挖掘

为了在事务数据库中发现关联规则,在现实挖掘应用中,经常采用不同的标准去判断不同项目的重要性,管理项目之间的分类关系和处理定量数据集这3个方法去处理问题,因此提出一个在定量事务数据库中采用多最小支持度,在项目集中获取隐含知识的多层模糊关联规则挖掘算法。该挖掘算法使用两种支持度约束和至上而下逐步细化的方法推导出频繁项集,同时可以发现交叉层次的模糊关联规则。通过实例证明了该挖掘算法在多最小支持度约束下推导出的多层模糊关联规则是易于理解和有意义的,具有很好的效率和伸缩性。     

加权模糊关联规则的研究

1 引言关联规则是展示属性-值频繁地在给定的数据集中一起出现的条件,最常见的是对大型超市的事务数据库进行货篮分析,文[1]提出了解决此类问题的布尔型属性关联规则的Apriori算法。数量关联在股市分析、银行存款分析和医疗诊断等众多方面都有重要应用价值。数量关联用来描述数量型属性特征之间的相互关系,用数量型关联规则来表示,如“10％年龄在50-70之间的已婚人员至少拥有两辆汽车”。文[2]首先讨论数量型关联规则,文中的挖掘算法将数量型属性划分成多个区间,但这样的方法会引起划分边界过硬的缺点。     

一种飞行数据的模糊关联规则挖掘算法*

提出利用模糊属性集和关联规则的支持度获得高效率的关联规则增量更新挖掘的方法。首先对输入数据集进行模糊离散化,确定相应的模糊属性集,模糊支持数和各属性原先的模糊聚类中心;然后检查是否满足最小支持度条件,将其添加到更新后的模糊频繁属性集集合中;最后比较模糊频繁属性集和负边界的变化,得到最终更新后的模糊频繁属性集和相应的关联规则。采用实际飞行数据验证了该算法可以避免反复和多层扫描数据库的时间消耗问题,模糊关联规则挖掘算法可以高效和准确提取增量关联规则。     

基于最大值约束的模糊关联规则挖掘

数据挖掘是从数据库中发现潜在有用知识或者感兴趣模式的过程。在数据挖掘领域中主要集中于单一支持度下的关联规则挖掘,在事务数据库中发现项目之间的关联性,而在实际应用中,项目可以有不同的最小支持度,不同的项目可能具有不同的标准去判断其重要性,因此提出一个在最大值支持度约束下,发现有用的模糊关联规则挖掘算法,在该约束下,利用逐层搜索的迭代方法发现频繁项目集,通过实例证明了该挖掘算法是易于理解和有意义的,具有很好的效率。     

模糊关联规则算法在入侵检测中的应用

提出了一种结合Apriori和Kuok's算法的改进的模糊关联规则算法.在定义隶属函数、决策树结构和规则集相似度的基础上,采用改进的挖掘算法挖掘数值属性的关联规则.实验结果表明,算法在规则生成和时间效率方面都显示了良好的性能.