An Incremental Learning Method for Data Mining from Large Databases

Knowledge Discovery techniques seek to find new information about a domain through a combination of existing domain knowledge and data examples from the domain. These techniques can either be manually performed by an expert, or automated using software algorithms (Machine Learning). However some domains, such as the clinical field of Lung Function testing, contain volumes of data too vast and detailed for manual analysis to be effective, and existing knowledge too complex for Machine Learning algorithms to be able to adequately discover relevant knowledge. In many cases this data is also unclassified, with no previous analysis having been performed. A better approach for these domains might be to involve a human expert, taking advantage of their expertise to guide the process, and to use Machine Learning techniques to assist the expert in discovering new and meaningful relationships in the data. It is hypothesised that Knowledge Acquisition methods would provide a strong basis for such a Knowledge Discovery method, particularly methods which can provide incremental verification and validation of knowledge as it is obtained. This study examines how the MCRDR (Multiple Classification Ripple- Down Rules) Knowledge Acquisition process can be adapted to develop a new Knowledge Discovery method, Exposed MCRDR, and tests this method in the domain of Lung Function. Preliminary results suggest that the EMCRDR method can be successfully applied to discover new knowledge in a complex domain, and reveal many potential areas of study and development for the MCRDR method

Legal Knowledge Acquisition Using Case-Based Reasoning and Model Inference

Although Case-Based Reasoning comes out in order to solve knowledge acquisition bottleneck, a case structure acquisition bottleneck has emerged, superseding it. Because we cannot decide an appropriate case structure in advance, a framework for CBR should be able to improve a case structure dynamically, collecting and analyzing cases. Here is discussed a new framework for knowledge acquisition using CBR and model inference. Model Inference tries to obtain new descriptors(predicates) with interaction of a domain expert, regarding the predicate as the slots that compose a case structure, with an eye to the function of theoretical term generation. The framework has two features: (1) CBR obtains a more suitable group of slots (a case structure) incrementally through cooperation with model inference, and (2) model inference with theoretical term capability discovers the rules which deal with a given task better. Furthermore, weevaluate the feasibility of the framework by implementing it to d..

A method for knowledge discovery and development with health data

One of the most overlooked problems in the field of knowledge discovery is the acquisition and incorporation of existing knowledge about the data being analysed (Fayyad, Piatetsky-Shapiro et al. 1996; Pohle 2003; Kotsifakos, Marketos et al. 2008; Marinica and Guillet 2009). Doing this efficiently and effectively can greatly improve the relevance and usefulness of the results discovered, particularly for complex domains with a large amount of existing knowledge (Adejuwon & Mosavi, 2010; C. Zhang, Yu, & Bell, 2009). This study applies the successful Multiple Classification Ripple Down Rules (MCRDR) knowledge acquisition method to build a knowledge base from a complex dataset of lung function data, and describes a method for utilising the dataset to provide additional knowledge validation. The method acquired knowledge successfully, but indicated that a focus on rule-driven knowledge acquisition may adversely affect the MCRDR process. Knowledge acquisition was performed with multiple domain experts, with separate knowledge bases successfully consolidated using an evidence-based method to quantify differences and resolve conflicts. This knowledge comparison method was also tested as a learning and assessment tool for a small group of medical students, with positive results. In addition, the consolidated expert knowledge base was applied to the analysis of the lung function data, with a set of common data mining techniques, to reproduce and expand on a group of published lung function studies. Results showed that new knowledge could be discovered effectively and efficiently in a complex domain, despite the user having little domain knowledge themselves. Results were supported by recent literature, and include findings that may be of interest in the respiratory field. Notably, newly discovered knowledge is automatically incorporated into the knowledge base, allowing incremental knowledge discovery and easy application of those discoveries