Frans Coenen

Graph mining is an important research area within the domain of data mining. The field of study concentrates on the identification of frequent subgraphs within graph data sets. The research goals are directed at: (i) effective mechanisms for generating candidate subgraphs (without generating duplicates) and (ii) how best to process the generated candidate subgraphs so as to identify the desired frequent subgraphs in a way that is computationally efficient and procedurally effective. This paper presents a survey of current research in the field of frequent subgraph mining and proposes solutions to address the main research issues.

Discovering how Mesenchymal Stem Cells (MSCs) can be differentiated is an important topic in stem cell therapy and tissue engineering. In a general context, such differentiation analysis can be modeled as a classification problem in data mining. Specifically, this is concerned with the single-label multi-class classification task. Previous studies on this topic suggests the Associative Classification (AC) rather than other alternative (Classification) techniques, and presented classification results based on the CMAR (Classification based on Multiple Association Rules) associative classifier. Other AC algorithms include: CBA (Classification Based on Associations), PRM (Predictive Rule Mining), CPAR (Classification based on Predictive Association Rules) and TFPC (Total From Partial Classification). The main aim of this chapter is to compare the performance of different associative classifiers, in terms of classification accuracy, efficiency, number of rules to be generated, quality of such rules, and the maximum number of attributes in rule-antecedents, with respect to MSC differentiation analysis.

The multi-agent paradigm for building intelligent systems has gradually been accepted by researchers and practitioners in the research field of artificial intelligence. There are also attempts of adapting agents and agent-based systems for creating industrial applications and providing e-services. In this paper, we present an attempt to use agents for constructing an online after-sale services system. The system is decomposed into four major cooperative agents, and in which each agent concentrates on particular aspects in the system and expresses intelligence by using various techniques. The proposed agent-based framework for the system is presented at both the micro-level and the macro-level according to the Gala methodology. UML notations are also used to represent some software design models. As the result of this, agents are implemented into a framework for which exploits case-based reasoning (CBR) technique to fulfil real life on-line services' diagnoses and tasks