Agents in dynamic environments have to deal with world representations that change over time. In order to allow agents to act autonomously and to make their decisions on a solid basis an interpretation of the current scene is necessary. If intentions of other agents or events that are likely to happen in the future can be recognized, the agent’s performance can be improved as it can adapt the b...

This paper presents application of data mining to data stored in a hospital information system in which temporal behavior of global hospital activities are visualized. The results show that the reuse of stored data will give a powerful tool for hospital management and lead to improvement of hospital services.

We introduce an algorithm for mining expressive temporal relationships from complex data. Our algorithm, AprioriSetsAndSequences (ASAS), extends the Apriori algorithm to data sets in which a single data instance may consist of a combination of attribute values that are nominal sequences, time series, sets, and traditional relational values. Data sets of this type occur naturally in many domains...

The Clinical e-Science Framework (CLEF) demonstrator runs Information Extraction technology over textual, narrative patient notes to assemble repositories of clinical patient data for the purposes of biomedical research and clinical care. Since many important medical events in the course of a patient’s treatment are mentioned in multiple documents and most documents will only include partial de...

We describe current research in temporal, spatial, and spatiotemporal data mining. In these types of data mining, a model of time, space, or space-time plays a nontrivial role. As an example of current research, we describe our MegaMiner prototype software. The DGG-Discover 5.2 module of MegaMiner is based on expected distribution domain generalization graphs (EDDGGs), which allow detailed doma...

Temporal data mining (TDM) has been attracting more and more interest from a vast range of domains, from engineering to finance. Similarity discovery technique concentrates on the evolution and development of data, attempting to discover the similarity regularity of dynamic data evolution. The most significant techniques developed in recent researches to deal with similarity discovery in TDM ar...

Intelligent tutoring systems (ITS) acquire rich data about students’ behavior during learning; data mining techniques can help to describe, interpret and predict student behavior, and to evaluate progress in relation to learning outcomes. We describe applications of data mining to challenges related to finding patterns in student actions at the level of single problems to predictions across ses...

This paper is concerned with the statistical development of our spatial-temporal data mining procedure, LASR (pronounced “laser”). LASR is the abbreviation for Longitudinal Analysis with Self-Registration of largep-small-n data. It was motivated by a study of “Neuromuscular Electrical Stimulation” experiments, where the data are noisy and heterogeneous, might not align from one session to anoth...

In this paper I define spatio-temporal regions as pairs consisting of a spatial and a temporal component and I define topological relations between them. Using the notion of rough sets I define approximations of spatio-temporal regions and relations between those approximations. Based on relations between approximated spatio-temporal regions configurations of spatio-temporal objects can be char...

Spatio-temporal data sets are often very large and difficult to analyze and display. Since they are fundamental for decision support in many application contexts, recently a lot of interest has arisen toward data-mining techniques to filter out relevant subsets of very large data repositories as well as visualization tools to effectively display the results. In this paper we propose a data-mini...