DENCAST: distributed density-based clustering for multi-target regression

Entropy-based Consensus for Distributed Data Clustering

The increasingly larger scale of available data and the more restrictive concerns on their privacy are some of the challenging aspects of data mining today. In this paper, Entropy-based Consensus on Cluster Centers (EC3) is introduced for clustering in distributed systems with a consideration for confidentiality of data; i.e. it is the negotiations among local cluster centers that are used in t...

Scalable Density-Based Distributed Clustering

Clustering has become an increasingly important task in analysing huge amounts of data. Traditional applications require that all data has to be located at the site where it is scrutinized. Nowadays, large amounts of heterogeneous, complex data reside on different, independently working computers which are connected to each other via local or wide area networks. In this paper, we propose a scal...

DBDC: Density Based Distributed Clustering

Clustering has become an increasingly important task in modern application domains such as marketing and purchasing assistance, multimedia, molecular biology as well as many others. In most of these areas, the data are originally collected at different sites. In order to extract information from these data, they are merged at a central site and then clustered. In this paper, we propose a differ...

Forecasting via Distributed Density-Based Clustering

Hierarchical Density-Based Clustering for Multi-Represented Objects

In recent years, the complexity of data objects in data mining applications has increased as well as their plain numbers. As a result, there exist various feature transformations and thus multiple object representations. For example, an image can be described by a text annotation, a color histogram and some texture features. To cluster thesemulti-represented objects, dedicated datamining algori...