able databases far outstrips the growth of corresponding knowledge. This creates both a need and an opportunity for extracting knowledge from databases. Many recent results have been reported on extracting different kinds of knowledge from databases, including diagnostic rules, drug side effects, classes of stars, rules for expert systems, and rules for semantic query optimization. The importan...

The paper introduces a novel problem based on causal modeling in marketing where knowledge discovery is able to provide useful results (as shown in a real-world application). The problem features (with uncertain data and available expert knowledge) and the proposed multiobjective optimization approach makes genetic fuzzy systems to be a good framework to deal with that.

A holistic approach, in the form of a process, is proposed in order to discover marketing intelligence from Internet data. The Internet-enabled knowledge discovery process contains the steps human resource identification, problem specification, data prospecting, domain knowledge elicitation, methodology identification, data pre-processing, pattern discovery, and knowledge post-processing. It al...

Current approaches to knowledge discovery can be differentiated based on the discovered models using the following criteria: effectiveness, understandability (to a user or expert in the domain) and evolvability (ability to adapt over time to a changing environment). Most current approaches satisfy understandability or effectiveness, but not simultaneously, while tending to ignore knowledge evol...

With the steady growth in electronic patient records and clinical medical informatics systems, the data collected for routine clinical use have been accumulating at a dramatic rate. Inter-disciplinary research provides a new generation of computation tools in knowledge discovery and data management is in great demand. In this study, an expert-guided decision tree construction strategy is propos...

declare that this thesis contains no material which has been accepted for the award of any other degree or diploma in any tertiary institution. To my knowledge and belief, this thesis contains no material previously published or written by another person except where due reference is made in the text of the thesis. Abstract Knowledge Discovery techniques seek to find new information about a dom...

This paper surveys our efforts in the automated analysis of human created digital artifacts and human computer interactions to enable peer-to-peer (P2P) knowledge management (KM). We begin outlining the relationships among knowledge management, peer-to-peer computing, collaboration, and human language technology. We first discuss tools to support peer and group knowledge discovery, exemplifying...

The use of process discovery algorithms is in practice hindered by many factors, being the algorithm’s representational bias, parameter configuration and algorithm’s capabilities the most important ones. Nowadays, a user of these algorithms needs an expert knowledge in order to successfully apply them. In this demo, we present the RS4PD, a recommender system that uses portfolio-based algorithm ...

This paper presents an exploration engine for text mining and crosscontext link discovery, implemented as a web application with a user-friendly interface. The system supports experts in advanced document exploration by facilitating document retrieval, analysis and visualization. It enables document retrieval from public databases like PubMed, as well as by querying the web, followed by documen...

Subgroup Discovery (SD) aims to find coherent, easy-to-interpret subsets of the dataset at hand, where something exceptional is going on. Since the resulting subgroups are defined in terms of conditions on attributes of the dataset, this data mining task is ideally suited to be used by non-expert analysts. The typical SD approach uses a heuristic beam search, involving parameters that strongly ...