industrial-scale r&d it projects depend on many sub-technologies which need to be understood and have their risks analysed before the project can begin for their success. when planning such an industrial-scale project, the list of technologies and the associations of these technologies with each other is often complex and form a network. discovery of this network of technologies is time con...

Time-stamped location information is regarded as spatio-temporal data due to its time and space dimensions and, by its nature, is highly vulnerable to misuse. Privacy issues related to collection, use and distribution of individuals’ location information are the main obstacles impeding knowledge discovery in spatio-temporal data. Suppressing identifiers from the data does not suffice since move...

This paper describes temporal data mining techniques for extracting information from temporal health records consisting of a time series of elderly diabetic patients’ tests. Diabetes is one of the most common diseases affecting quality of life and is potentially life threatening for elderly people in developed countries. We propose a data mining procedure to analyse these time sequences in thre...

The discovery of preferences in space and time is important in a variety of applications. In this paper we first establish the correspondence between a set of preferences in space and time and density estimates obtained from observations of spatial-temporal features recorded within large databases. We perform density estimation using both kernel methods and mixture models. The density estimates...

This paper presents a framework to support analysis and trend detection in historical data from Neonatal Intensive Care Unit (NICU) patients. The clinical research extensions contribute to fundamental data mining framework research through the integration of temporal abstraction and support of null hypothesis testing within the data mining processes. The application of this new data mining appr...

Graph pattern mining has played important roles in network analysis and information retrieval. However, temporal characteristics of networks have not been estimated sufficiently. We propose time graph pattern mining as a new concept of graph mining reflecting the temporal information of a network. We conduct two case studies of time graph pattern mining: extensively discussed topics on blog sit...

This research demonstrates the application of association rule mining to spatiotemporal data. Association rule mining seeks to discover associations among transactions encoded in a database. An association rule takes the form A ? B where A (the antecedent) and B (the consequent) are sets of predicates. A spatiotemporal association rule occurs when there is a spatio-temporal relationship in the ...

Today, multi-electrode arrays (MEAs) capture neuronal spike streams in real time, thus providing dynamic perspectives into brain function. Mining such spike streams from these MEAs is critical towards understanding the firing patterns of neurons and gaining insight into the underlying cellular activity. However, the acquisition rate of neuronal data places a tremendous computational burden on t...

The rapid increase in the data available leads to the difficulty for analyzing those data and different types of frameworks are required for unearthing useful knowledge that can be extracted from such databases. The field of temporal data mining is relatively young and one expects to see many new developments in the near future. In all data mining applications, the primary constraint is the lar...

In this paper we describe an application of our approach to temporal text mining in Competitive Intelligence for the biotechnology and pharmaceutical industry. The main objective is to identify changes and trends of associations among entities of interest that appear in text over time. Text Mining (TM) exploits information contained in textual data in various ways, including the type of analyse...