Nico Schlitter

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DenGraph-HO is an extension of the density-based graph clustering algorithm DenGraph. It is able to detect dense groups of nodes in a given graph and produces a hierarchy of clusters, which can be efficiently computed. The generated hierarchy can be used to investigate the structure and the characteristics of social networks. Each hierarchy level provides a(More)
The evaluation and benchmarking of NoSQL databases is challenging due to the variety of query languages and also due to the denormalized scheme that allows to include non-scalar values and repeatable structures such as nested documents. In the rather young age of NoSQL databases, there are not many studies on a conventional solution to enable a fair(More)
This article presents a new approach for privacy preserving neural network training. Several studies have been devoted to privacy preserving supervised model learning, but little work has been done to extend neural network learning with a privacy preserving protocol. Neural networks are popular for many applications, among else those calling for a robust(More)
This paper addresses privacy preserving classification for vertically partitioned datasets. We present an approach based on information hiding that is similar to the basic idea of microaggregation. We use a local clustering to mask the dataset of each party and replace the original attributes by cluster identifiers. That way, the masked datasets can be(More)
In this paper we present an application of our incremental graph clustering algorithm (DENGRAPH) on a data set obtained from the music community site The aim of our study is to determine the music preferences of people and to observe how the taste in music changes over time. Over a period of 130 weeks, we extract for each interval user profiles of(More)
For the analysis of communities in social networks several data mining techniques have been developed such as the DenGraph algorithm to study the dynamics of groups in graph structures. The here proposed DenGraph-HO algorithm is an extension of the density-based graph clusterer DenGraph. It produces a cluster hierarchy that can be used to implement a(More)
Traditionell werden an den Hochschulen lokale Speichersysteme betrieben. Eine kosteneffizientere Alternative stellen zentralisierte Speichersysteme dar, die allerdings über WAN-Verbindungen häufig nicht performant genutzt werden können. In dieser Arbeit werden deshalb Anforderungen für zukünftige Speichersysteme im Hochschulumfeld definiert und die(More)
In this work we evaluate the scalability and performance of our previously presented GenPAC method by applying it on larger datasets. The work is motivated by the necessity of meeting privacy constraints when focusing on the importance and broad application of data mining but also by the growing demand for privacy preservation in general. GenPAC, which can(More)
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