In this paper we propose a method for building possibilistic temporal constraint networks that better summarizes the huge set of mined timed-stamped sequences from a temporal data mining process. It belongs to the well-known second-order data mining problem, where the vast amount of simple sequences or patterns needs to be summarized further. It is a very important topic because the huge number...

The advent of web-based applications and services has created such diverse and voluminous web log data stored in web servers, proxy servers, client machines, or organizational databases. This paper attempts to investigate the effect of temporal attribute in relational rule mining for web log data. We incorporated the characteristics of time in the rule mining process and analysed the effect of ...

This paper focusses on designing and applying data mining techniques to analyze spatial and spatiotemporal data originated in scientific domains. Data mining is the process of discovering hidden and meaningful knowledge in a data set. It has been successfully applied to many real-life problems, for instance, web personalization, network intrusion detection, and customized Marketing. This paper ...

Research in the field of knowledge discovery from temporal data recently focused on a new type of data: interval sequences. In contrast to event sequences interval sequences contain labeled events with a temporal extension. Mining frequent temporal patterns from interval sequences proved to be a valuable tool for generating knowledge in the automotive business. In this paper we propose a new al...

Temporal association rule mining promises the ability to discover time-dependent correlations or patterns between events in large volumes of data. To date, most temporal data mining research has focused on events existing at a point in time rather than over a temporal interval. In comparison to static rules, mining with respect to time points provides semantically richer rules. However, accommo...

Longitudinal data consist of the repeated measurements of some variables which describe a process (or phenomenon) over time. They can be analyzed to unearth information on the dynamics of the process. In this paper we propose a temporal data mining framework to analyze these data and acquire knowledge, in the form of temporal patterns, on the events which can frequently trigger particular stage...

Advances in wireless transmission and mobile technology applied to LBS (Location-based Services) flood us with amounts of moving objects data. Vast amounts of gathered data from position sensors of mobile phones, PDAs, or vehicles hide interesting and valuable knowledge and describe the behavior of moving objects. The correlation between temporal moving patterns of moving objects and geo-featur...

To analyze the laboratory data by data mining, user-centered universal tools have not been available in medicine. We analyzed 1,565,877 laboratory data of 771 patients with viral hepatitis in order to find the difference of the temporal changes in laboratory test data between Hepatitis B and Hepatitis C by the combination of temporal abstraction and data mining. The data for one patient is temp...

Technological advances in terms of data acquisition enable to better monitor dynamic phenomena in various domains (areas, fields) including environment. The collected data is more and more complex spatial, temporal, heterogeneous and multi-scale. Exploiting this data requires new data analysis and knowledge discovery methods. In that context, approaches aimed at discovering spatio-temporal patt...

Temporal association rule mining promises the ability to discover time-dependent correlations or patterns between events in large volumes of data. To date, most temporal data mining research has focused on events existing at a point in time rather than over a temporal interval. In comparison to static rules, mining with respect to time points provides semantically richer rules. However, accommo...