Yannis Batistakis

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Cluster analysis aims at identifying groups of similar objects and, therefore helps to discover distribution of patterns and interesting correlations in large data sets. It has been subject of wide research since it arises in many application domains in engineering, business and social sciences. Especially, in the last years the availability of huge(More)
Clustering is an unsupervised process since there are no predefined classes and no examples that would indicate grouping properties in the data set. The majority of the clustering algorithms behave differently depending on the features of the data set and the initial assumptions for defining groups. Therefore, in most applications the resulting clustering(More)
Clustering results validation is an important topic in the context of pattern recognition. We review approaches and systems in this context. In the first part of this paper we presented clustering validity checking approaches based on internal and external criteria. In the second, current part, we present a review of clustering validity approaches based on(More)
Clustering is mostly an unsupervised procedure and most of the clustering algorithms depend on assumptions and initial guesses in order to define the subgroups presented in a data set. As a consequence, in most applications the final clusters require some sort of evaluation. The evaluation procedure has to tackle difficult problems, which can be(More)
Clustering aims at discovering groups and identifying interesting distributions and patterns in data sets. Researchers have extensively studied clustering since it arises in many application domains in engineering and social sciences. In the last years the availability of huge transactional and experimental data sets and the arising requirements for data(More)
Spatial data mining is a mining knowledge from large amounts of spatial data. Spatial data mining algorithms can be separated into four general categories: clustering and outlier detection, association and co-location method, trend detection and classification. All these methods have been compared according to various attributes. This paper introduces the(More)
In the present study, antibiotic resistance data generated in Greece by the WHONET Network were further analyzed by the use of data mining techniques. More specifically association rules were extracted among data collected in the Microbiology Dept. of “Sismanoglion” General Hospital, a 500-bed general hospital, in Athens, Greece. The data studied were the(More)
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