TY - JOUR
T1 - Tracing evolving subspace clusters in temporal climate data
AU - Günnemann, Stephan
AU - Kremer, Hardy
AU - Laufkötter, Charlotte
AU - Seidl, Thomas
N1 - Funding Information:
Acknowledgements We thank the Alfred Wegener Institute for Polar and Marine Research for providing the Oceanographic Grid Data. This article has been supported by the UMIC Research Centre, RWTH Aachen University.
PY - 2012/3
Y1 - 2012/3
N2 - Analysis of temporal climate data is an active research area. Advanced data mining methods designed especially for these temporal data support the domain expert's pursuit to understand phenomena as the climate change, which is crucial for a sustainable world. Important solutions for mining temporal data are cluster tracing approaches, which are used to mine temporal evolutions of clusters. Generally, clusters represent groups of objects with similar values. In a temporal context like tracing, similar values correspond to similar behavior in one snapshot in time. Each cluster can be interpreted as a behavior type and cluster tracing corresponds to tracking similar behaviors over time. Existing tracing approaches are for datasets satisfying two specific conditions: The clusters appear in all attributes, i.e., fullspace clusters, and the data objects have unique identifiers. These identifiers are used for tracking clusters by measuring the number of objects two clusters have in common, i.e. clusters are traced based on similar object sets. These conditions, however, are strict: First, in complex data, clusters are often hidden in individual subsets of the dimensions. Second, mapping clusters based on similar objects sets does not reflect the idea of tracing similar behavior types over time, because similar behavior can even be represented by clusters having no objects in common. A tracing method based on similar object values is needed. In this paper, we introduce a novel approach that traces subspace clusters based on object value similarity. Neither subspace tracing nor tracing by object value similarity has been done before.
AB - Analysis of temporal climate data is an active research area. Advanced data mining methods designed especially for these temporal data support the domain expert's pursuit to understand phenomena as the climate change, which is crucial for a sustainable world. Important solutions for mining temporal data are cluster tracing approaches, which are used to mine temporal evolutions of clusters. Generally, clusters represent groups of objects with similar values. In a temporal context like tracing, similar values correspond to similar behavior in one snapshot in time. Each cluster can be interpreted as a behavior type and cluster tracing corresponds to tracking similar behaviors over time. Existing tracing approaches are for datasets satisfying two specific conditions: The clusters appear in all attributes, i.e., fullspace clusters, and the data objects have unique identifiers. These identifiers are used for tracking clusters by measuring the number of objects two clusters have in common, i.e. clusters are traced based on similar object sets. These conditions, however, are strict: First, in complex data, clusters are often hidden in individual subsets of the dimensions. Second, mapping clusters based on similar objects sets does not reflect the idea of tracing similar behavior types over time, because similar behavior can even be represented by clusters having no objects in common. A tracing method based on similar object values is needed. In this paper, we introduce a novel approach that traces subspace clusters based on object value similarity. Neither subspace tracing nor tracing by object value similarity has been done before.
UR - http://www.scopus.com/inward/record.url?scp=84856612030&partnerID=8YFLogxK
U2 - 10.1007/s10618-011-0237-7
DO - 10.1007/s10618-011-0237-7
M3 - Article
AN - SCOPUS:84856612030
SN - 1384-5810
VL - 24
SP - 387
EP - 410
JO - Data Mining and Knowledge Discovery
JF - Data Mining and Knowledge Discovery
IS - 2
ER -