模糊聚类在数量型关联规则提取中的应用

关系数据库中数量属性的关联规则挖掘问题是经常要遇到的问题。该文利用改进的FCM进行模糊聚类，主要是解决FCM算法的局部极小问题。利用聚类的结果可以使数量型属性关联规则向类别型属性转换，类别型属性再转化为布尔型属性，这样，便可以从许多关联规则的挖掘方法中找出有意义的规则。     

关系数据库快速分块的一种实现策略

本文提出一种对关系数据库进行快速分块的原理和方法，研究了分块属性规则存储和约束条件规范化问题，给出了实现分块的算法描述。     

关系数据库中关联规则挖掘的一种高效算法

近年来，关系数据库被越来越多的行业采用，大量的生产、管理、科研等信息被收集存储，因此在关系数据库中进行有效的关联规则挖掘的需求日益增强。文中根据事务数据库中布尔型关联规则挖掘的相关理论和方法，在分析了关系数据库中关联规则挖掘具有的特殊性的基础上，从利用结构化查询语言(SQL)对关系数据库简便而高效的操作出发，提出了一种在关系数据库中挖掘多值型、多维型关联规则的简易算法。实验证明该算法具有较高的执行效率和一定的实用性。     

区间值关系数据库上模糊关联规则的预测方法

讨论了区间值关系数据库上模糊关联规则的挖掘算法与预测方法。采用一种比RFCM算法省时的FCMdd算法将记录在属性的取值划分成若干个模糊集,并提出区间值关系数据库上模糊关联规则的挖掘算法。仿真实例说明挖掘算法能够通过挖掘有意义的模糊关联规则来发现区间值关系数据库中蕴涵的关联性。区间值关系数据库上模糊关联规则的预测方法改进了标准可加性模型,并通过遗传算法调整模糊关联规则中三角模糊数的参数来提高预测的精度。     

挖掘集合值关系数据库的模糊关联规则

为了挖掘集合值关系数据库的模糊关联规则，应用竞争聚集算法将记录在数量型属性上的取值划分成若干个模糊集，接着给出集合值关系数据库上数量型属的模糊关联规则的挖掘算法，此算法能将数量型属性模糊关联规则的挖掘问题转化为布尔属性关联规则的挖掘问题。最后通过一个实例说明挖掘算法的合理性。     

基于SQL的多值多层关联规则挖掘

在分析研究关系数据库上关联规则挖掘现有方法的基础上,提出了一种基于结构化查询语言SQL的多值多层关联规则挖掘新方法。采用了一种新的根据概念分层的编码方法对多值属性进行离散化,然后利用SQL的查询语句,结合多值属性的编码,实现了关系数据库上的多层关联规则挖掘。实验表明,该算法具有快速、有效、易开发等优点。     

基于SQL的多值多层关联规则挖掘

在分析研究关系数据库上关联规则挖掘现有方法的基础上,提出了一种基于结构化查询语言SOL的多值多层关联规则挖掘新方法.采用了一种新的根据概念分层的编码方法对多值属性进行离散化,然后利用SOL的查询语句,结合多值属性的编码,实现了关系数据库上的多层关联规则挖掘.实验表踢,该算法具有快速、有效、易开发等优点.     

一种基于分类一致性的决策规则获取算法

提出一种基于分类一致性的规则获取算法．它是一种例化方向的方法，即从空集开始，以条件属性子集的分类一致性来度量属性的重要性，逐步加入重要的属性，当选择的属性子集能够正确分类时，则获取到决策规则．算法中设计了一个规则约简过程，用来简化所获得的规则，增强规则的泛化能力．实验结果表明，所提出的算法获得的规则更为简洁和高效．     

遗传算法在数量型关联规则提取中的应用

关系数据库中数量属性的关联规则挖掘问题是关联规则挖掘中经常要遇到的问题。该文利用遗传算法解决FCM模糊聚类问题主要是为了避免FCM算法的局部极小问题。利用聚类的结果可以使数量型属性关联规则转换成类别型属性,类别型属性再转化为布尔型属性,这样,即可以使用许多已有关联规则挖掘方法挖掘出有意义的规则。     

关联规则在房地产广告媒体选择中的应用

针对目标客户群选择合适的广告媒体,可以用较低的广告费用获得较好的广告促销效果。论文提出了将关联规则挖掘应用于广告媒体选择的观点,对房地产公司决策型关系数据库中的属性转换问题进行了研究,用一种快速关联规则挖掘算法对房地产公司决策型关系数据库进行了挖掘,得出了一些规则。     

A NEW METHOD TO GENERATE FUZZY RULES FROM RELATIONAL DATABASE SYSTEMS FOR ESTIMATING NULL VALUES

Fuzzy decision trees can be used to generate fuzzy rules from training instances to deal with forecasting and classification problems. We propose a new method to construct fuzzy decision trees from relational database systems and to generate fuzzy rules from the constructed fuzzy decision trees for estimating null values, where the weights of attributes are used to derive the values of certainty factors of the generated fuzzy rules. We use the concept of "coefficient of determination" of the statistics to derive the weights of the attributes in relational database systems and use the normalized weights of the attributes to derive the values of certainty factors of the generated fuzzy rules. Furthermore, we also use regression equations of the statistics to construct a complete fuzzy decision tree for generating better fuzzy rules. The proposed method obtains a higher average estimated accuracy rate than the existing methods for estimating null values in relational database systems.     

Generating weighted fuzzy rules from relational database systems for estimating values using genetic algorithms

In recent years, some methods have been proposed to estimate values in relational database systems. However, the estimated accuracy of the existing methods are not good enough. In this paper, we present a new method to generate weighted fuzzy rules from relational database systems for estimating values using genetic algorithms (GAs), where the attributes appearing in the antecedent part of generated fuzzy rules have different weights. After a predefined number of evolutions of the GA, the best chromosome contains the optimal weights of the attributes, and they can be translated into a set of rules to be used for estimating values. The proposed method can get a higher average estimated accuracy rate than the methods we presented in two previous papers.     

A new approach to generate weighted fuzzy rules using genetic algorithms for estimating null values

In this paper, we present a new method to generate weighted fuzzy rules using genetic algorithms for estimating null values in relational database systems, where there are negative functional dependency relationships between attributes. The proposed method can get higher average estimated accuracy rates than the method presented in [Chen, S. M., & Huang, C. M. (2003). Generating weighted fuzzy rules from relational database systems for estimating null values using genetic algorithms. IEEE Transactions on Fuzzy Systems, 11(4), 495–506].     

Data-driven discovery of quantitative rules in relational databases

A quantitative rule is a rule associated with quantitative information which assesses the representativeness of the rule in the database. An efficient induction method is developed for learning quantitative rules in relational databases. With the assistance of knowledge about concept hierarchies, data relevance, and expected rule forms, attribute-oriented induction can be performed on the database, which integrates database operations with the learning process and provides a simple, efficient way of learning quantitative rules from large databases. The method involves the learning of both characteristic rules and classification rules. Quantitative information facilitates quantitative reasoning, incremental learning, and learning in the presence of noise. Moreover, learning qualitative rules can be treated as a special case of learning quantitative rules. It is shown that attribute-oriented induction provides an efficient and effective mechanism for learning various kinds of knowledge rules from relational databases     

Discovering Maximal Generalized Decision Rules Through Horizontal and Vertical Data Reduction

We present a method to learn maximal generalized decision rules from databases by integrating discretization, generalization and rough set feature selection. Our method reduces the data horizontally and vertically. In the first phase, discretization and generalization are integrated and the numeric attributes are discretized into a few intervals. The primitive values of symbolic attributes are replaced by high level concepts and some obvious superfluous or irrelevant symbolic attributes are also eliminated. Horizontal reduction is accomplished by merging identical tuples after the substitution of an attribute value by its higher level value in a pre-defined concept hierarchy for symbolic attributes, or the discretization of continuous (or numeric) attributes. This phase greatly decreases the number of tuples in the database. In the second phase, a novel context-sensitive feature merit measure is used to rank the features, a subset of relevant attributes is chosen based on rough set theory and the merit values of the features. A reduced table is obtained by removing those attributes which are not in the relevant attributes subset and the data set is further reduced vertically without destroying the interdependence relationships between classes and the attributes. Then rough set-based value reduction is further performed on the reduced table and all redundant condition values are dropped. Finally, tuples in the reduced table are transformed into a set of maximal generalized decision rules. The experimental results on UCI data sets and a real market database demonstrate that our method can dramatically reduce the feature space and improve learning accuracy.     

GENERATING FUZZY RULES FROM RELATIONAL DATABASE SYSTEMS FOR ESTIMATING NULL VALUES

This paper presents a new algorithm for constructing fuzzy decision trees from relational database systems and generating fuzzy rules from the constructed fuzzy decision trees. We also present a method for dealing with the completeness of the constructed fuzzy decision trees. Based on the generated fuzzyrules, we also present a method for estimating null values in relational database systems. The proposed methods provide a useful way to estimate null values in relational database systems.