横向拆分形势背景下的快速规则提取方法

概念格是进行数据挖掘和规则提取的一种有效工具。目前已经提出的概念格上的规则提取方法大多是针对整个形式背景,得到的规则数目较多,规则集规模较大,且这种规则结构不便于两个规则集的合并。针对这个问题,本文提出一种伪规则的概念,并给出渐近式获取伪规则的方法;同时证明了通过伪规则集,用户可以根据自己的兴趣有选择地从伪规则集合中产生出所需的蕴含规则;提出了将两个伪规则集进行合并的方法,从而用户可以通过拆分合并的思想来获取规则集;最后通过实验分析验证了算法的有效性。     

基于概念格的组规则产生集的算法研究

数据挖掘中传统的关联规则生成算法产生的关联规则集合相当庞大,其中很多规则可由其它规则导出。使用闭项集可以减少规则的数目,而概念格节点间的泛化和例化关系非常适用于规则的提取。目前几种基于概念格的规则提取算法局限于得到准确支持度、信任度的无冗余规则。提出了一种在概念格上挖掘出能推导出所有满足最小支持度、信任度规则的规则产生集算法,文中称之为组规则产生集算法,减少了规则的规模。在此基础上进一步给出了组规则产生集的存储数据结构并用其导出一般规则产生集的算法。     

工作流业务规则语义的完整性验证技术

工作流模型的验证技术主要包括语法验证、结构验证和语义验证,其中语义验证是层次最高、最为严格的验证,验证的范围十分广泛,也是难点所在,目前尚缺乏有效的方法.而且,语义的正确性会影响工作流模型的控制逻辑.也是结构合理性的影响因素之一.从工作流模型表达的语义出发,通过分析工作流模型刻画的业务规则以及相应的约束集部分,基于对约束集语义的形式化,问题转换为对约束集语义的完整性验证.如果工作流模型中的条件节点所描述的约束集语义有遗漏、冗余或者无意义,也决定了模型错误的拓扑结构.提出全域覆盖性判定定理及基于判定树的验证算法.通过验证工作流业务规则语义的完整性,对工作流模型结构的合理性也给予了保证.这种验证方法具有很强的通用性.不依赖于具体的建模方法,适用范围广泛.     

概念格上规则产生集的算法研究与应用

用传统的规则生成算法产生的关联规则集合相当庞大,其中很多规则可由其它规则导出。使用闭项集可以减少规则的数目,而概念格节点间的泛化和例化关系非常适用于规则的提取。目前几种基于概念格的规则提取算法局限于得到准确支持度、信任度的无冗余规则。提出了一种在概念格上挖掘出能推导出所有满足最小支持度、信任度规则的规则产生集算法,文中称之为组规则产生集算法,减少了规则的规模,提高了挖掘效率,进一步给出了组规则产生集的存储数据结构和根据应用需要用其导出单一后项规则的算法。     

基于工作流日志的决策规则挖掘研究*

为了挖掘工作流日志中的决策规则信息,分析了工作流日志中的数据属性如何影响工作流实例的路径选择。基于算法挖掘工作流日志过程模型,对过程模型中的决策点进行分析,通过决策树分析技术结合工作流日志中的数据属性挖掘出影响工作流实例路由的决策规则。分析了现实应用中决策规则挖据所遇到的问题,并提出解决算法。最后通过测试程序测试并验证了挖掘过程。测试结果表明该算法能够正确地挖掘出决策规则。     

规则库的合并以及冗余、环路和冲突的检测

针对知识库的建立需要耗费大量的时间和人力,同时相同或相似领域的知识库数量越来越多,提出利用现有规则知识库进行合并生成一个新的规则知识库,并对生成的新规则知识库进行知识冗余、环路和冲突的检测算法。首先,规则库利用有向超图来表示;其次,将有向超图利用其邻接矩阵来表示,那么规则库的合并可以转换成有向超图所对应的邻接矩阵的合并,并依据邻接矩阵求可达矩阵以及利用总可达矩阵来检测规则库中规则的冗余、环路和冲突。最后,算法的有效性通过实例加以验证。     

决策蕴涵上的推理规则和推理过程研究

形式概念分析是用于概念分析和可视化的偏序集理论。决策蕴涵是形式概念分析在决策情形下的知识表示。已有研究从逻辑角度分析了决策蕴涵,并给出了完整的语义描述和语构描述,其中在语构方面已经有一个完备的推理规则集,即扩增推理规则和合并推理规则。在此基础上,提出了新的推理规则——后件合并推理规则,证明了其合理性,以及与扩增推理规则组成的推理规则集的完备性和无冗余性;通过研究扩增推理规则和后件合并推理规则的性质,给出了使用这两条推理规则从完备集推导其对应封闭集的有效方法等理论结果,为进一步的算法研究与应用以及更深入的理论研究工作奠定基础。     

一种集成数据挖掘的自动视频分类方法

针对自动视频分类工作中分类预测精度低的问题，提出了一种集成数据挖掘技术的自动视频分类方法。首先进行视频分割，形成了一个视频属性数据库；然后分别使用决策树、分类关联规则等技术对视频属性数据库进行数据挖掘，提取出决策树分类规则集和分类关联规则集；最后利用一个规则集的合并裁减算法来合并这两个分类预测规则集，形成最终的具有更高精度的视频分类规则集。通过实验验证了决策树分类预测规则和分类关联规则具有分类预测的一致性；同时实验表明，使用合并后的规则集比单独使用一个规则集来预测视频具有更高的预测准确率。     

不一致性决策信息系统规则获取方法研究

针对不一致性决策信息系统,提出一种使用规则分辨矩阵获取决策规则的算法.不一致性决策信息系统在约简时可能产生新的冲突规则,选择冲突规则对不变的约简并产生规则,在此基础上进行不一致性规则的合并从而生成正确的规则集.     

有效的矩阵加权正负关联规则挖掘算法——MWARM-SRCCCI

针对现有加权关联规则挖掘算法不能适用于矩阵加权数据的缺陷,给出一种新的矩阵加权项集剪枝策略,构建矩阵加权正负关联模式评价框架SRCCCI,提出一种新的基于SRCCCI评价框架的矩阵加权正负关联规则挖掘算法MWARM-SRCCCI。该算法克服了现有挖掘技术的缺陷,采用新的剪枝技术和模式评价方法,挖掘有效的矩阵加权正负关联规则,避免一些无效和无趣的模式产生。以中文Web测试集CWT200g为实验数据,与现有无加权正负关联规则挖掘算法比较,MWARM-SRCCCI算法的挖掘时间减幅最大可达74.74%。理论分析和实验结果表明,MWARM-SRCCCI算法具有较好的剪枝效果,候选项集数量和挖掘时间明显减少,挖掘效率得到极大提高,其关联模式可为信息检索提供可靠的查询扩展词来源。     

钻井液设计专家系统规则库的检测算法

钻井液设计专家系统规则库的规模随着规则的更新与日俱增,对规则库的维护工作变得日益重要。针对规则库的从属、冗余、环路和冲突等问题提出一种检测算法。引入有向超图来表示规则库中的规则;用邻接矩阵表示该有向超图,并计算出它的可达矩阵和总可达矩阵;用总可达矩阵对规则库进行检测,找出规则库中存在的问题。实验表明,与已有的检测算法相比,该算法能够有效地检测出规则库中存在的问题,同时构建的邻接矩阵规模较小,在保证算法简洁的基础上提高了效率。     

基于二叉决策图的网络可达性计算

针对现有攻击图生成方法中普遍通过网络扫描获得网络可达性信息存在信息不完整、耗时长、产生网络干扰等不足,提出一种基于二叉决策图的网络可达性计算方法。该方法利用二叉决策图建模防火墙规则,通过高效的集合运算计算网络可达性。真实环境检测和模拟实验均表明该方法具有精确、耗时短、无网络干扰等优点,适用于大规模网络可达性的计算,推动了攻击图在大规模网络中的应用。     

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.     

Data mining with Temporal Abstractions: learning rules from time series

A large volume of research in temporal data mining is focusing on discovering temporal rules from time-stamped data. The majority of the methods proposed so far have been mainly devoted to the mining of temporal rules which describe relationships between data sequences or instantaneous events and do not consider the presence of complex temporal patterns into the dataset. Such complex patterns, such as trends or up and down behaviors, are often very interesting for the users. In this paper we propose a new kind of temporal association rule and the related extraction algorithm; the learned rules involve complex temporal patterns in both their antecedent and consequent. Within our proposed approach, the user defines a set of complex patterns of interest that constitute the basis for the construction of the temporal rule; such complex patterns are represented and retrieved in the data through the formalism of knowledge-based Temporal Abstractions. An Apriori-like algorithm looks then for meaningful temporal relationships (in particular, precedence temporal relationships) among the complex patterns of interest. The paper presents the results obtained by the rule extraction algorithm on a simulated dataset and on two different datasets related to biomedical applications: the first one concerns the analysis of time series coming from the monitoring of different clinical variables during hemodialysis sessions, while the other one deals with the biological problem of inferring relationships between genes from DNA microarray data.     

Binary classification rule generation from decomposed data

Learning classification rules from data that do not fit in the available memory is a challenging task. The goal of this study is to develop an approach for generating binary classification rules from decomposed data that are equivalent in terms of quality to those found over the whole data. In the proposed approach, each class is divided into the same arbitrary small number of subtables. For each pair of subsets from different classes, rule sets are induced using any sequential covering algorithm. Rule sets generated from the same positive class subset and different negative class subsets are merged using an operator constructed on the basis of Cartesian product and conjunction operators. The rule sets obtained in this way are joined into one set. During the rule merging, unnecessary rules are removed. It is proven that for training data, the quality of the rule set generated using the approach is the same as that for the whole data. It is experimentally verified that for test data, the quality of classification is comparable with that obtained using a nondecomposed data approach.     

Model generation by domain refinement and rule reduction

The granularity and interpretability of a fuzzy model are influenced by the method used to construct the rule base. Models obtained by a heuristic assessment of the underlying system are generally highly granular with interpretable rules, while models algorithmically generated from an analysis of training data consist of a large number of rules with small granularity. This paper presents a method for increasing the granularity of rules while satisfying a prescribed precision bound on the training data. The model is generated by a two-stage process. The first step iteratively refines the partitions of the input domains until a rule base is generated that satisfies the precision bound. In this step, the antecedents of the rules are obtained from decomposable partitions of the input domains and the consequents are generated using proximity techniques. A greedy merging algorithm is then applied to increase the granularity of the rules while preserving the precision bound. To enhance the representational capabilities of a rule and reduce the number of rules required, the rules constructed by the merging procedure have multi-dimensional antecedents. A model defined with rules of this form incorporates advantageous features of both clustering and proximity methods for rule generation. Experimental results demonstrate the ability of the algorithm to reduce the number of rules in a fuzzy model with both precise and imprecise training information.     

汉语概率型上下文无关语法的自动推导

本文提出了一种汉语概率型上下文无关语法的自动推导方法，它在匹配分析机制上实现了无指导的ＥＭ迭代训练算法，并通过对训练语料的自动短语界定预处理以及在集成不同知识源基础上构造合适始规则集     

A TSK-type neurofuzzy network approach to system modeling problems.

We develop a neurofuzzy network technique to extract TSK-type fuzzy rules from a given set of input-output data for system modeling problems. Fuzzy clusters are generated incrementally from the training dataset, and similar clusters are merged dynamically together through input-similarity, output-similarity, and output-variance tests. The associated membership functions are defined with statistical means and deviations. Each cluster corresponds to a fuzzy IF-THEN rule, and the obtained rules can be further refined by a fuzzy neural network with a hybrid learning algorithm which combines a recursive singular value decomposition-based least squares estimator and the gradient descent method. The proposed technique has several advantages. The information about input and output data subspaces is considered simultaneously for cluster generation and merging. Membership functions match closely with and describe properly the real distribution of the training data points. Redundant clusters are combined, and the sensitivity to the input order of training data is reduced. Besides, generation of the whole set of clusters from the scratch can be avoided when new training data are considered.     

Mining the optimal class association rule set

We define an optimal class association rule set to be the minimum rule set with the same predictive power of the complete class association rule set. Using this rule set instead of the complete class association rule set we can avoid redundant computation that would otherwise be required for mining predictive association rules and hence improve the efficiency of the mining process significantly. We present an efficient algorithm for mining the optimal class association rule set using an upward closure property of pruning weak rules before they are actually generated. We have implemented the algorithm and our experimental results show that our algorithm generates the optimal class association rule set, whose size is smaller than 1/17 of the complete class association rule set on average, in significantly less rime than generating the complete class association rule set. Our proposed criterion has been shown very effective for pruning weak rules in dense databases.     

含有非独立型触发环的主动规则集归约算法研究

可终止性判定问题是主动数据库的一个核心问题。现有的研究工作提出了运用触发图和活化图的方法解决这个问题，其中的一个关键技术就是利用归约算法对主动规则集进行归约。已有的计算方法对一些可归约规则无法识别。本文提出了独立型触发环、非独立型触发环、活化路径、禁止活化环、禁止活化规则等概念。基于这些概念，提出了一个新的归约算法，从而可识别出更多的可归约规则。