In this paper, we propose a new method, namely EFI-Mine, for mining temporal emerging frequent itemsets from data streams efficiently and effectively. The temporal emerging frequent itemsets are those that are infrequent in the current time window of data stream but have high potential to become frequent in the subsequent time windows. Discovery of emerging frequent itemsets is an important pro...

Most methods for temporal pattern mining assume that time is represented by points in a straight line starting at some initial instant. Discovering sequential patterns in customer’s transactions is a well-known application where such data mining methods have been used successfully. In this paper, we consider a new kind of temporal pattern where both interval and punctual time representation are...

In real life, media information has time attributes either implicitly or explicitly known as temporal data. This paper investigates the usefulness of applying Bayesian classification to an interval encoded temporal database with prioritized items. The proposed method performs temporal mining by encoding the database with weighted items which prioritizes the items according to their importance f...

Medical knowledge includes frequently occurring temporal patterns in longitudinal patient records. These patterns are not easily detectable by human clinicians. Current knowledge could be extended by automated temporal data mining. However, multivariate time-oriented data are often present at various levels of abstraction and at multiple temporal granularities, requiring a transformation into a...

In this paper we presents the data mining and integration of environmental applications in EU IST project ADMIRE. It briefly presents the project ADMIRE and data mining of spatio-temporal data in general. The application, originally targeting flood simulation and prediction is now being extended into the broader context of environmental studies. We describe several interesting scenarios, in whi...

Predictive data mining in clinical medicine deals with learning models to predict patients’ health. The models can be devoted to support clinicians in diagnostic, therapeutic, or monitoring tasks. Data mining methods are usually applied in clinical contexts to analyze retrospective data, thus giving healthcare professionals the opportunity to exploit large amounts of data routinely collected du...

Mining frequent patterns in databases is a fundamental and essential problem in data mining research. A continuity is a kind of causal relationship which describes a definite temporal factor with exact position between the records. Since continuities break the boundaries of records, the number of potential patterns will increase drastically. An alternative approach is to mine closed frequent co...

Understanding the functioning of a neural system in terms of its underlying circuitry is an important problem in neuroscience. Recent developments in electrophysiology and imaging allow one to simultaneously record activities of hundreds of neurons. Inferring the underlying neuronal connectivity patterns from such multi-neuronal spike train data streams is a challenging statistical and computat...

The focus of this survey is on spatio-temporal data mining and database retrieval for visual traffic surveillance systems. In many traffic surveillance applications, such as incident detection, abnormal events detection, vehicle speed estimation, and traffic volume estimation, the data used for reasoning is really in the form of spatio-temporal data (e.g. vehicle trajectories). How to effective...

Recently, there has been a lot of interest in temporal granularity, and its applications in temporal dependency theory and data mining. Generalization hierarchies used in multi-dimensional databases and OLAP serve a role similar to that of time granularity in temporal databases, but they also apply to non-temporal dimensions, like space. In this paper, we first generalize temporal functional de...