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Due to the ever growing presence of data streams, there has been a considerable amount of research on stream mining algorithms. While many algorithms have been introduced that tackle the problem of clustering on evolving data streams, hardly any attention has been paid to appropriate evaluation measures. Measures developed for static scenarios, namely(More)
In today's applications, massive, evolving data streams are ubiquitous. Massive Online Analysis (MOA) is a software environment for implementing algorithms and running experiments for online learning from evolving data streams. MOA is designed to deal with the challenging problems of scaling up the implementation of state of the art algorithms to real world(More)
Data mining techniques extract interesting patterns out of large data resources. Meaningful visualization and interactive exploration of patterns are crucial for knowledge discovery. Visualization techniques exist for traditional clustering in low dimensional spaces. In high dimensional data, clusters typically only exist in subspace projections. This(More)
—In today's applications, evolving data streams are ubiquitous. Stream clustering algorithms were introduced to gain useful knowledge from these streams in real-time. The quality of the obtained clusterings, i.e. how good they reflect the data, can be assessed by evaluation measures. A multitude of stream clustering algorithms and evaluation measures for(More)
In high dimensional databases, traditional full space clustering methods are known to fail due to the curse of dimensionality. Thus, in recent years, subspace clustering and projected clustering approaches were proposed for clustering in high dimensional spaces. As the area is rather young, few comparative studies on the advantages and disadvantages of the(More)
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