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.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Notes
- 1.
We have added at small random constant to each membership curve in order to better separate the curves in the graphic. The actual membership degrees without the constant in graphic never exceed 1.
References
Klawonn F (2106) Exploring data sets for clusters and validating single clusters. Procedia Comput Sci 96 (1381–1390)
Kerr G, Ruskin H, Crane M (2008) Techniques for clustering gene expression data. Comput Biol Med 38(3):383–393
Bair E (2013) Semi-supervised clustering methods. Wiley Interdiscip Rev Comput Stat 5:349–361
Ernst J, Nau G, Bar-Joseph Z (2005) Clustering short time series gene expression data. Bioinform 21:159–168
Ernst J, Bar-Joseph Z (2006) Stem: a tool for the analysis of short time series gene expression data. BMC Bioinform 7:191. doi:10.1186/1471-2105-7-191
Georgieva O, Klawonn F (2006) Evolving clustering via the dynamic data assigning assessment algorithm. In: Proceedings IEEE international symposium on evolving fuzzy systems. pp 95–100
Georgieva O, Klawonn F (2006) Cluster analysis via the dynamic data assigning assessment algorithm. Inf Technol Control 2:14–21
Georgieva O, Klawonn F (2008) Dynamic data assigning assessment clustering of streaming data. App Soft Comput 8:1305–1313
Dunn J (1973) A fuzzy relative of the isodata process and its use in detecting compact well-separated clusters. Cybern Syst 3(3):32–57
Bezdek J (1981) Pattern recognition with fuzzy objective function algorithms. Plenum Press, New York
Davé RN (1991) Characterization and detection of noise in clustering. Pattern Recognit Lett 12:406–414
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2018 Springer International Publishing AG
About this chapter
Cite this chapter
Klawonn, F. (2018). Exploring Time-Resolved Data for Patterns and Validating Single Clusters. In: Mostaghim, S., Nürnberger, A., Borgelt, C. (eds) Frontiers in Computational Intelligence. Studies in Computational Intelligence, vol 739. Springer, Cham. https://doi.org/10.1007/978-3-319-67789-7_5
Download citation
DOI: https://doi.org/10.1007/978-3-319-67789-7_5
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-67788-0
Online ISBN: 978-3-319-67789-7
eBook Packages: EngineeringEngineering (R0)