“Just Accepted” papers have undergone full peer review and have been accepted for publication in Radiology: Artificial Intelligence. This article will undergo copyediting, layout, and proof review ...

Traditional clustering methods were developed to analyse complete data sets. Faults during the data collection, data transfer or data cleaning often lead to missing values in data so that common clustering methods can not be used for the data analysis. Therefore, in these cases clustering methods which can handle missing values in data are of great use. In this paper we discuss different approaches proposed in the literature for adapting partitioning clustering algorithms for dealing with missing values in data. We analyse them on two appropriate data sets and compare them with each other. We give particular attention to the analysis of the accuracy of these methods depending on the different missing-data mechanisms and the percentage of missing values in the data sets.

This paper addresses the issue of analyzing time series with temporal association rule mining techniques. Since originally association rule mining was developed for the analysis of transactional data, as it occurs for instance in market basket analysis, algorithms and time series have to be adapted in order to apply these techniques gainfully to the analysis of time series in general. Continuous time series of different origins can be discretized in order to mine several temporal association rules, what reveals interesting coherences in one and between pairs of time series. Depending on the domain, the knowledge about these coherences can be used for several purposes, e.g. for the prediction of future values of time series. We present a short review on different standard and temporal association rule mining approaches and on approaches that apply association rule mining to time series analysis. In addition to that, we explain in detail how some of the most interesting kinds of temporal association rules can be mined from continuous time series and present an prototype implementation. We demonstrate and evaluate our implementation on two large datasets containing river level measurement and stock data.

Despite all the efforts to build up a Semantic Web, where each machine can understand and interpret the data it processes, information is usually still stored in ordinary relational databases. Semantic Web applications needing access to such semantically unexploited data, have to create their own manual relational database to Semantic Web mappings. In this paper we analyze, whether the combination of Relational.OWL as a Semantic Web representation of relational databases and a semantic query language like SPARQL could be an alternative. The benefits of such an approach are clear, since it enables Semantic Web applications to access and query data actually stored in relational databases using their own built-in functionality.