关联规则是数据挖掘中一种重要的模式，Apriori算法是挖掘关联规则的典型算法，而Apriori算法存在一定的缺点：数据库的全局搜索和产生大项集时使用支持度阈值会删除有意义的规则等。Maradbcm算法是在KDD内在机理研究的基础上提出的一种新的挖掘关联规则算法，它可以克服Apriori算法的上述缺点。在简要地叙述了双库协同机制和Maradbcm算法后，将该算法应用于蘑菇数据库，结果显示该算法是有效的。它充分显示了内在机理研究对KDD主流发展的重要作用与影响，并为整个知识发现系统的研究提供了一条全新的路径。

探讨围绕知识发现领域中较为宏观、较为重大的问题。首先，根据复杂类型数据（包括Web数据、多媒体数据、空间数据、时间序列数据等）所具有的非线性动力学性质和特征，采用模式（定义为Hilbert空间中的矢量）来定量地表征复杂类型数据的多变性及具有的不确定状态和行为，并用模式的变化来刻画其整体知识发现过程的发展和演变规律；其次，以知识发现系统内在机理的研究为基础，构造了复杂类型数据知识发现系统的总体结构模型——发现特征子空间模型DFSSM；最后，用基于Web的文本挖掘系统和基于图像信息（气象云图） 的知识发现系统作为实例进行了验证，结果表明DFSSM方法对于非结构化的文本数据及图像数据类型的知识发现过程具有指导性作用。因此，该结构模型具有较好的实用性与普适性，有望拓展到其他复杂类型数据的知识发现过程中。

The inner mechanisms were put forward solely by the author Yang Bingru. Among the inner mechanisms the double-base cooperating mechanism, double-basis fusion mechanism and information increasing mechanism are introduced in this paper. The expansibility structural models of KD (Knowledge discovery) are described including KDD*, KDK*, KD (D&K) and DFSSM, etc.. Some innovative methods, such as a mining algorithm and auto-evaluation method are introduced. On the basis of all above parts, KDTIM (Knowledge discovery theory based on inner mechanism) theory is the advanced theory, and explaination of the mainstream driving to KD brought byresearch on inner mechanism and KDTIM is presented.

KDD (Knowledge Discovery Based on Database) and KDK (Knowledge Discovery Based on Knowledge Base) are the different cognitive field and discovery process. In most people's view, they are independent each other. In this paper we can summarize the following tasks: 1) We discussed that two kinds of the Structure Model and Mining Algorithm of KDK based on facts and rules in Knowledge Base. 2) We proves that the inherent relation between KDD and KDK (i.e. Double-Basis Fusion Mechanism). 3) Then we gained the new structural model and implementation technology of KDK*. 4) We are produced and the self-structural of two discovery processes are fused into the structural model of KD (D&K) of complex system. 5) The imitation experimentation proved that the validity of above mechanism and structural model.

在KDD(knowledge discovery in database)中，对所发现的知识进行评价是一个很重要的环节。提出了一种针对KDD中因果关联规则的自动评价方法.该评价方法采用了全新的、有效的知识表示方法（语言场和语言值结构）和推理机制（因果关系定性推理机制），并且具有通用性和交互性的特征。给出了此评价方法的理论依据和构造过程，并提供了相应的算法。通过对具体实例的运行检验，证明了此评价方法的有效性.通过与相关工作的比较，证明了其先进性。

在“KDD中双库协同机制的研究（Ⅰ）”的理论基础上，实现了对知识库的简约并实现了双库协同机制的两个协调器，从而建立了由KDD融入双库协同机制的新结构模型-KDD*，这是一个在结构和功能上相对于KDD而言的开放的、优化的扩体。基于双库协同机制与KDD*，提出了挖掘关联规则和聚类规则的新算法，充分体现了内在机理研究对主流发展的影响，开辟了一条全新的研究方向与路径。

针对KDD（基于数据库的知识发现）主流发展中存在的典型问题提出了用知识库去制约与驱动数据库，并通过数据库改善知识库结构的知识发现的新思想，形成了具有双库协同机制的KDD 的开放系统KDD*，从而提高了知识发现的速度、精度和认知自主性，并使知识库在结构上具备了实时维护与自我进化的能力，同时阐述了作为双库协同基础的数据库和知识库在本质上的对应关系。

KD (D&K) (Synthesized Knowledge Discovery System based on Database and Knowledge Base Cooperating Mechanism) is first advanced in this paper on the basis of KDD (Knowledge Discovery System based on Database) and KDK (Knowledge Discovery System based on Knowledge Base). KD (D&K) is not simple addition of KDD and KDK but a new system with absolutely new character and essential development, which is distinct from KDD and KDK and includes them.Not only the generalized structural frame of control rules KD (D&K) acquiring method is proposed, but also the theoretical basis of its key technical problem-double-bases cooperating mechanism is discussed according to the original academic idea to restrict KDD by basic knowledge base.

A new framework of knowledge representation of fuzzy language field and fuzzy language value structure is shown, Then the generalized cell automation that can synthetically process fuzzy indeterminacy and random indeterminacy and generalized inductive logic causal model are put forward. On this basis, the new logic indeterminacy causal inductive automatic reasoning mechanism which is based on fuzzy state description is presented. At the end of this paper its application in the development of intelligent controller is discussed.

A new structure of ESKD (expert system based on knowledge discovery system KD (D&K)) is first presented on the basis of KD (D&K)-a synthesized knowledge discovery system based on double-base (database and knowledge base) cooperating mechanism. With all new features, ESKD may form a new, research direction and provide a great probability for solving the wealth of knowledge in the knowledge base. The general structural frame of ESKD and some sub-systems, among ESKD have been described, and the dynamic knowledge base based on double-base cooperating mechanism has been emphased on. According to the result of demonstrative experiment. the structure of ESKD is effective and feasible.