Healthcare sector provides huge volume of data on patients and their illnesses, on health insurance plants, medication and treatment schedules for different diseases, on medical services and so forth. Nowadays, there is a growing demand for the healthcare community to transform the existing quantities of healthcare data into value-added data, by discovering unknown patterns and relations betwee...

Sample-based clustering is one of the most common methods for discovering disease subtypes as well as unknown taxonomies. By revealing hidden structures in microarray data, cluster analysis can potentially lead to more tailored therapies for patients as well as better diagnostic procedures. In this work, we present a novel method for automatically discovering clusters of samples which are coher...

Due to the complexity of real-world applications, the number of databases and the volume of data have increased tremendously. Discovering qualitative and quantitative patterns from databases in such a distributed information-providingenvironment has been recognized as a challenging task. In response to such a demand, data mining and data warehousing techniques are emerging to extract the previo...

Transcription factors are key regulatory elements that control gene expression. Recognition of transcription factor binding site (TFBS) motifs in the upstream region of coexpressed genes is therefore critical towards a true understanding of the regulations of gene expression. The task of discovering eukaryotic TFBSs remains a challenging problem. Here, we demonstrate that evolutionary computati...

Historical evidence in some regions of Latin America has suggested that the system of ancient routes between places could have determined the success or collapse of prehistoric societies. The identification of such routes provided essential information to understand initial conditions in the evolution of the actual road infrastructure. Looking into the increasing technology applied to generate ...

This paper presents the results of a photogrammetric survey rock art panel Tanum 247:1 in Kalleby, which revealed an entirely new boat that had previously been missed documentation history over 50 years long. Through combined use digital and traditional methods could be verified. It is therefore argued collating documentations, both past present, can help to create better picture Bronze Age car...

Mapping the functional use of city areas (e.g., mapping clusters of hotels or of electronic shops) enables a variety of applications (e.g., innovative way-finding tools). To do that mapping, researchers have recently processed geo-referenced data with spatial clustering algorithms. These algorithms usually perform two consecutive steps: they cluster nearby points on the map, and then assign lab...

In this paper, we are interested in discovering genetic and environmental factors that are involved in multifactorial diseases. Experiments have been achieved by the Biological Institute of Lille and many data has been generated. To exploit these data, data mining tools are required and we propose a two-phase optimisation approach using a speci®c genetic algorithm. During the ®rst step, we sele...

Complex Event Processing (CEP) is emerging as a new paradigm for continuous processing of streaming data in order to detect relevant information and provide support for timely reactions. The main role of a CEP engine is to detect the occurrence of event patterns on the incoming streaming data. However, the problem of discovering the event patterns, although strongly related to the data mining f...

Spatial data mining is the process of discovering interesting and previously unknown, but potentially useful patterns from large spatial datasets. Extracting interesting and useful patterns from spatial datasets is more difficult than extracting the corresponding patterns from traditional numeric and categorical data due to the complexity of spatial data types, spatial relationships, and spatia...