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Published in:
2018 | OriginalPaper | Chapter
Exploring Time-Resolved Data for Patterns and Validating Single Clusters
Author : Frank Klawonn
Published in: Frontiers in Computational Intelligence
Publisher: Springer International Publishing
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Abstract
Cluster analysis is often described as the task to partition a data set into subset—called clusters—so that similar data objects belong to the same cluster and data objects from different clusters are not very similar. However, partitioning the whole data set into clusters is often not the aim when clustering algorithms are applied. Instead, the main goal is sometimes to find a few “good” clusters containing a limited amount of data objects, while even the majority of data objects might not be assigned to any cluster, contradicting the principle of partitioning the data set into clusters. In this paper, we revisit a method called dynamic data assigning assessment clustering to discover and validate single clusters in a data set and extend the dynamic data assigning assessment approach to the context of time-resolved data.