This work it is intended to discover classification rules for diagnosing certain pathologies. These rules are capable of discriminating among 12 different pathologies, whose main symptom is chest pain. In order to discover these rules it was used genetic programming as well as some concepts of data mining, particularly the emphasis on the discovery of comprehensible knowledge.

KDD is a rapidly expanding field with promise for great applicability. Knowledge discovery became the new database technology for the incoming years. The need for automated discovery tools caused an explosion in the number and type of tools available commercially and in the public domain. These requirements encouraged us to propose a new KDD model so called ODBC_KDD(2) described in [39] ."One o...

This work it is intended to discover classification rules for diagnosing certain pathologies. These rules are capable of discriminating among 12 different pathologies, whose main symptom is chest pain. In order to discover these rules it was used genetic programming as well as some concepts of data mining, particularly the emphasis on the discovery of comprehensible knowledge.

the aim of this study was to compare the performance of analogy (implicit),explicit and discovery learning in the retention and transfer of a topspin shot underpsychological pressure. for this purpose, 36 volunteer high school students wererandomly allocated to three treatment conditions: explicit, analogy, and discoverylearning. after performing 300 trials in the acquisition phase, an immediat...

Adding the dimension of time to databases produces time series databases (TSDB) and introduces new aspects and difficulties to data mining and knowledge discovery. In this correspondence, we introduce a general methodology for knowledge discovery in TSDB. The process of knowledge discovery in TSDR includes cleaning and filtering of time series data, identifying the most important predicting att...

Association rules are ”if-then rules” with two measures which quantify the support and confidence of the rule for a given data set. Having their origin in market basked analysis, association rules are now one of the most popular tools in data mining. This popularity is to a large part due to the availability of efficient algorithms following from the development of the Apriori algorithm. In the...

Studies on fish embryo models are widely developed in research. They are used in several research fields including drug discovery or environmental toxicology. In this article, we propose an entirely automated assay to detect cardiac arrest in Medaka (Oryzias latipes) based on image analysis. We propose a multi-scale pipeline based on mathematical morphology. Starting from video sequences of ent...

Our goal is to design a knowledge discovery tool that has the ability to accurately generate rules using concepts and structured data values extracted from semi-structured documents. To date, two of our major contributions have been the design of a system architecture that facilitates the discovery of rules from HTML documents and the development of an efficient association rule algorithm that ...

On account of the enormous amounts of rules that can be produced by data mining algorithms, knowledge post-processing is a difficult stage in an association rule discovery process. In order to find relevant knowledge, the user needs to rummage through the rules. To make this task easier, we propose a new interactive mining methodology based on well-adapted dynamic visual representations. It all...

With some significant exceptions, current applications for data mining are either in those areas for which there is little accepted discovery methodology or are being used within a knowledge discovery process that does not expect authoritative results but finds the discovered rules useful none-the-less. This is in contrast to its application in the fields applicable to spatial or spatio-tempora...