Utilizing Genetic Algorithms to Optimize Membership Functions for Fuzzy Weighted Association Rules Mining

It is not an easy task to know a priori the most appropriate fuzzy sets that cover the domains of quantitative attributes for fuzzy association rules mining. In general, it is unrealistic that experts can always provide such sets. And finding the most appropriate fuzzy sets becomes a more complex problem when items are not considered to have equal importance and the support and confidence parameters required for the association rules mining process are specified as linguistic terms. Existing clustering based automated methods are not satisfactory because they do not consider the optimization of the discovered membership functions. In order to tackle this problem, we propose Genetic Algorithms (GAs) based clustering method, which dynamically adjusts the fuzzy sets to provide maximum profit based on user specified linguistic minimum support and confidence terms. This is achieved by tuning the base values of the membership functions for each quantitative attribute with respect to two different evaluation functions maximizing the number of large itemsets and the average of the confidence intervals of the generated rules. To the best of our knowledge, this is the first effort in this direction. Experiments conducted on 100 K transactions from the adult database of United States census in year 2000 demonstrate that the proposed clustering method exhibits good performance in terms of the number of produced large itemsets and interesting association rules.     

Multi-objective genetic algorithm based approaches for mining optimized fuzzy association rules

Association rules form one of the most widely used techniques to discover correlations among attribute in a database. So far, some efficient methods have been proposed to obtain these rules with respect to an optimal goal, such as: to maximize the number of large itemsets and interesting rules or the values of support and confidence for the discovered rules. This paper first introduces optimized fuzzy association rule mining in terms of three important criteria; strongness, interestingness and comprehensibility. Then, it proposes multi-objective Genetic Algorithm (GA) based approaches for discovering these optimized rules. Optimization technique according to given criterion may be one of two different forms; The first tries to determine the appropriate fuzzy sets of quantitative attributes in a prespecified rule, which is also called as certain rule. The second deals with finding both uncertain rules and their appropriate fuzzy sets. Experimental results conducted on a real data set show the effectiveness and applicability of the proposed approach.     

Mining Fuzzy Multiple-Level Association Rules from Quantitative Data

Machine-learning and data-mining techniques have been developed to turn data into useful task-oriented knowledge. Most algorithms for mining association rules identify relationships among transactions using binary values and find rules at a single-concept level. Transactions with quantitative values and items with hierarchical relationships are, however, commonly seen in real-world applications. This paper proposes a fuzzy multiple-level mining algorithm for extracting knowledge implicit in transactions stored as quantitative values. The proposed algorithm adopts a top-down progressively deepening approach to finding large itemsets. It integrates fuzzy-set concepts, data-mining technologies and multiple-level taxonomy to find fuzzy association rules from transaction data sets. Each 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 original items. The algorithm therefore focuses on the most important linguistic terms for reduced time complexity.     

Decentralized Association Rule Mining on Web Using Rough Set Theory

A central part of many algorithms for mining association rules in large data sets is a procedure that is to find so called frequent itemsets. The frequent itemsets are very large due to transactions data increasing. This paper proposes a new approach to find frequent itemsets employing rough set theory that can extract association rules for each homogenou.s cluster of transaction data records and relationships between different clusters. This paper conducts an algorithm to reduce a large number of itemsets to find valid association rules.     

频繁项集挖掘的研究进展及主流方法

关联分析作为数据挖掘的主要研究模块之一,主要用于发现隐藏在大型数据集中的强关联特征。而多数关联规则挖掘任务可分为频繁模式(频繁项集、频繁序列、频繁子图)的产生和规则的产生。前者发现数据集中满足最小支持度阈值的项集、序列与子图;后者从上一步发现的频繁模式中提取高置信度的规则。频繁项集挖掘是许多数据挖掘任务中的关键问题,也是关联规则挖掘算法的核心。十几年来,学者们致力于提高频繁项集的生成效率,从不同的角度进行改进以提高算法效率,大量的高效可伸缩性算法被提出。文中对频繁项集挖掘进行深入分析,对完全频繁项集、闭频繁项集、极大频繁项集的典型算法进行介绍和评述,最后对频繁项集挖掘算法的研究方向进行简要分析。     

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.     

Genetic-Fuzzy Data Mining With Divide-and-Conquer Strategy

Data mining is most commonly used in attempts to induce association rules from transaction data. Most previous studies focused on binary-valued transaction data. Transaction data in real-world applications, however, usually consist of quantitative values. This paper, thus, proposes a fuzzy data-mining algorithm for extracting both association rules and membership functions from quantitative transactions. A genetic algorithm (GA)-based framework for finding membership functions suitable for mining problems is proposed. The fitness of each set of membership functions is evaluated by the fuzzy-supports of the linguistic terms in the large 1-itemsets and by the suitability of the derived membership functions. The evaluation by the fuzzy supports of large 1-itemsets is much faster than that when considering all itemsets or interesting association rules. It can also help divide-and-conquer the derivation process of the membership functions for different items. The proposed GA framework, thus, maintains multiple populations, each for one item's membership functions. The final best sets of membership functions in all the populations are then gathered together to be used for mining fuzzy association rules. Experiments are conducted to analyze different fitness functions and set different fitness functions and setting different supports and confidences. Experiments are also conducted to compare the proposed algorithm, the one with uniform fuzzy partition, and the existing one without divide-and-conquer, with results validating the performance of the proposed algorithm.     

An effective approach for maintenance of pre-large-based frequent-itemset lattice in incremental mining

Incremental mining has attracted the attention of many researchers due to its usefulness in online applications. Many algorithms have thus been proposed for incrementally mining frequent itemsets. Maintaining a frequent-itemset lattice (FIL) is difficult for databases with large numbers of frequent itemsets, especially huge databases, due to the storage of links of nodes in the lattice. However, generating association rules from a FIL has been shown to be more effective than traditional methods such as directly generating rules from frequent itemsets or frequent closed itemsets. Therefore, when the number of frequent itemsets is not huge (i.e., they can be stored in the lattice without excessive memory overhead), the lattice-based approach outperforms approaches which mine association rules from frequent itemsets/frequent closed itemsets. However, incremental algorithms for building FILs have not yet been proposed. This paper proposes an effective approach for the maintenance of a FIL based on the pre-large concept in incremental mining. The building process of a FIL is first improved using two proposed theorems regarding the paternity relation between two nodes in the lattice. An effective approach for maintaining a FIL with dynamically inserted data is then proposed based on the pre-large and the diffset concepts. The experimental results show that the proposed approach outperforms the batch approach for building a FIL in terms of execution time.     

基于项目集知识库的关联规则挖掘与更新的高效算法

通过对已有的诸关联规则挖掘与更新算法进行深入的分析和研究，指出了其共同存在的问题与不足，提出了一种基于项目集知识库的关联规则挖掘与更新方法。该方法既适应当数据库D中数据不变而用户指定的最小支持度和最小置信度这两个阈值变化的情况，也适合事务数据库D中数据发生变化的情况。当事务数据库D中数据不变时，仅需扫描数据库一次，便可建立项目集知识库KBD，然后可反复调整最小支持度和最小置信度进行关联规则挖掘与更新。而当事务数据库D中数据发生变化时，仅需扫描数据集d 和d-各一次；通过对项目集知识库KBD的更新来达到对频繁项目集和关联规则的更新。     

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。     

Incremental fuzzy temporal association rule mining using fuzzy grid table

Traditional temporal association rules mining algorithms cannot dynamically update the temporal association rules within the valid time interval with increasing data. In this paper, a new algorithm called incremental fuzzy temporal association rule mining using fuzzy grid table (IFTARMFGT) is proposed by combining the advantages of boolean matrix with incremental mining. First, multivariate time series data are transformed into discrete fuzzy values that contain the time intervals and fuzzy membership. Second, in order to improve the mining efficiency, the concept of boolean matrices was introduced into the fuzzy membership to generate a fuzzy grid table to mine the frequent itemsets. Finally, in view of the Fast UPdate (FUP) algorithm, fuzzy temporal association rules are incrementally mined and updated without repeatedly scanning the original database by considering the lifespan of each item and inheriting the information from previous mining results. The experiments show that our algorithm provides better efficiency and interpretability in mining temporal association rules than other algorithms.

     

A CMFFP-tree algorithm to mine complete multiple fuzzy frequent itemsets

In the past, many algorithms were proposed to adopt fuzzy-set theory for discovering fuzzy association rules from quantitative databases. The fuzzy frequent pattern (FFP)-tree and the compressed fuzzy frequent pattern (CFFP)-tree algorithms were respectively proposed to mine the incomplete fuzzy frequent itemsets from the tree-based structures. In the past, multiple fuzzy frequent pattern (MFFP)-tree algorithm was proposed to keep more linguistic terms for mining fuzzy frequent itemsets. Since the MFFP-tree algorithm inherits the property of the FFP-tree algorithm, numerous tree nodes are thus required to build the MFFP-tree structure for mining the desired multiple fuzzy frequent itemsets. In this paper, the compressed multiple fuzzy frequent pattern (CMFFP)-tree algorithm is designed to keep not only the linguistic term with maximum membership value but also the other frequent linguistic terms for mining the completely fuzzy frequent itemsets. In the designed CMFFP-tree algorithm, the multiple frequent linguistic terms are sorted in descending order of their occurrence frequencies to build the CMFFP-tree structure. The construction process is the same as the CFFP-tree algorithm except more information are kept for later mining process to discover the completely fuzzy frequent itemsets. Each node in the CMFFP-tree uses the additional array to keep the membership values of its prefix path by intersection operation. A CMFFP-mine algorithm is also designed to efficiently mine the multiple fuzzy frequent itemsets from the developed CMFFP-tree structure. Experiments are then conducted to show the performance of the proposed CMFFP-tree algorithm in terms of execution time and the number of tree nodes, compared to those of the MFFP-tree and CFFP-tree algorithms.     

An efficient cluster and decomposition algorithm for mining association rules

Conventional algorithms for mining association rules operate in a combination of smaller large itemsets. This paper presents a new efficient which combines both the cluster concept and decomposition of larger candidate itemsets, while proceeds from mining the maximal large itemsets down to large 1-itemsets, named cluster-decomposition association rule (CDAR). First, the CDAR method creates some clusters by reading the database only once, and then clustering the transaction records to the kth cluster, where the length of a record is k. Then, the large k-itemsets are generated by contrasts with the kth cluster only, unlike the combination concept that contrasts with the entire database. Experiments with real-life databases show that CDAR outperforms Apriori, a well-known and widely used association rule.     

Toward boosting distributed association rule mining by data de-clustering

Existing parallel algorithms for association rule mining have a large inter-site communication cost or require a large amount of space to maintain the local support counts of a large number of candidate sets. This study proposes a de-clustering approach for distributed architectures, which eliminates the inter-site communication cost, for most of the influential association rule mining algorithms. To de-cluster the database into similar partitions, an efficient algorithm is developed to approximate the shortest spanning path (SSP) to link transaction data together. The SSP obtained is then used to evenly de-cluster the transaction data into subgroups. The proposed approach guarantees that all subgroups are similar to each other and to the original group. Experiment results show that data size and the number of items are the only two factors that determine the performance of de-clustering. Additionally, based on the approach, most of the influential association rule mining algorithms can be implemented in a distributed architecture to obtain a drastic increase in speed without losing any frequent itemsets. Furthermore, the data distribution in each de-clustered participant is almost the same as that of a single site, which implies that the proposed approach can be regarded as a sampling method for distributed association rule mining. Finally, the experiment results prove that the original inadequate mining results can be improved to an almost perfect level.     

应用于入侵检测系统的报警关联的改进Apriori算法

在众多的关联规则挖掘算法中,Apriori算法是最为经典的一个,但Apriori算法有以下缺陷：需要扫描多次数据库、生成大量候选集以及迭代求解频繁项集。提出了一种一步交集操作得到最大频繁项目集的方法。支持度由交集的次数得到而无需再去扫描事务数据库,将其中一些属性进行编号能减少存储空间且方便搜索候选集列表,从而提高算法的效率。最后针对入侵检测系统形成关联规则。实验结果表明,优化后的算法能有效地提高关联规则挖掘的效率。     

基于双阈值Apriori算法和非频繁项集的关联规则挖掘方法

针对从本文数据集中的正负关联规则挖掘问题,提出一种基于双阈值Apriori算法和非频繁项集的挖掘方法。首先,对通过逆文档频率(IDF)对语料库中的项(项集)进行加权,筛选出前N%的项集。然后,通过提出的双支持度阈值Apriori算法来提取频繁项集和非频繁项集,以此降低非频繁项集的数量。最后,通过置信度和升降度阈值的判断,分别从频繁项集和非频繁项集中挖掘正负关联规则。其中,创新性的利用了非频繁项集来挖掘正负关联规则。在一个医学文本数据集上的实验结果表明,提出的方法能够有效挖掘出正负关联规则,且能够大大降低项集和规则数量。     

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.     

一种基于聚类分析与关联规则的联合挖掘算法

本文分析了基于关联规则的Apriori算法及其存在的缺陷,提出了一种将聚类分析和关联规则相结合的联合数据挖掘算法。在大项集数量相等时,联合挖掘算法明显优于Apriori算法。     

分布式环境下约束性关联规则的快速挖掘

研究人员针对单机环境提出了约束性关联规则的挖掘算法,但它们不适用于分布式环境.为此本文讨论分布式环境下约束性关联规则的快速挖掘技术,提出一种基于分布式环境的约束性关联规则快速挖掘算法DCAR,其中包括局部约束性频繁项目集挖掘算法MLFC和全局约束性频繁项目集挖掘算法MGFC.该算法根据布尔约束条件产生向导集,采用一种新的候选项集生成函数Reorder-gen,该函数通过向导集高效地产生分布式环境中满足约束条件的、数量较少且完备的候选项集,并且求解全局约束性频繁项集过程中,传送局部候选项集支持数的通信量为O(n),从而提高了算法的挖掘效率.将本文提出的算法加以实现,实验结果表明DCAR算法高效可行,其效率大约是DMA-IC算法的2-3倍.     

基于生成子的频繁项集聚类算法

如何有效地约简频繁项集的数量是目前数据挖掘研究的热点。对频繁项集进行聚类是该问题的解决方法之一。由于生成子是全体频繁项集的无损精简表示,故对生成子进行聚类与对全体频繁项集进行聚类具有相同的效果。提出了一种基于生成子的频繁项集聚类算法。首先,利用最小描述长度原理,讨论了选择生成子进行聚类的合理性;其次,给出了生成子的剪枝策略及挖掘算法;最后,在一种新的项集相似性的度量标准的基础上,给生成子的聚类算法。实验结果表明,该方法可有效地减少项集的数量,并具有较高的挖掘效率。