Scaling up data mining algorithms for data of both high dimensionality and cardinality has
been lately recognized as one of the most challenging problems in data mining research …

Cluster analysis across multiple institutions poses significant challenges due to data-sharing
restrictions. To overcome these limitations, we introduce the Federated One-shot Ensemble …

Contemporary data-intensive applications generate large datasets of very high
dimensionality. Data management in high-dimensional spaces presents problems, such as …

Data mining tasks results are usually improved by reducing the dimensionality of data. This
improvement however is achieved harder in the case that data lay on a non linear manifold …

This study unit is about machine learning and neural networks provide means for computer
systems to extract useful information out of data. These techniques are widely used in the …

An increasing number of contemporary applications produce massive volumes of very high
dimensional data. In scientific databases, for example, it is common to encounter large sets …

In most information retrieval systems, software processes (whether agent-based or not)
reason about passive items of data. An alternative approach instantiates each record as an …

Due to the vast amount and pace of high-dimensional data production, dimensionality
reduction emerges as an important requirement in many application areas. In this paper, we …

In most information retrieval systems, software processes (whether agent-based or not)
reason about passive items of data. An alternative approach instantiates each piece of …

As data generation becomes increasingly inherently distributed, either due to user-
generated (multimedia) content or because of application-specific needs (sensor networks …