Automatic subspace clustering of high dimensional data for data mining applications
SIGMOD '98 Proceedings of the 1998 ACM SIGMOD international conference on Management of data
Trajectory clustering with mixtures of regression models
KDD '99 Proceedings of the fifth ACM SIGKDD international conference on Knowledge discovery and data mining
A Monte Carlo algorithm for fast projective clustering
Proceedings of the 2002 ACM SIGMOD international conference on Management of data
Discovering Similar Multidimensional Trajectories
ICDE '02 Proceedings of the 18th International Conference on Data Engineering
Frequent-Pattern based Iterative Projected Clustering
ICDM '03 Proceedings of the Third IEEE International Conference on Data Mining
Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining
On Change Diagnosis in Evolving Data Streams
IEEE Transactions on Knowledge and Data Engineering
MONIC: modeling and monitoring cluster transitions
Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining
A framework for clustering evolving data streams
VLDB '03 Proceedings of the 29th international conference on Very large data bases - Volume 29
A framework for projected clustering of high dimensional data streams
VLDB '04 Proceedings of the Thirtieth international conference on Very large data bases - Volume 30
Detecting and Tracking Spatio-temporal Clusters with Adaptive History Filtering
ICDMW '08 Proceedings of the 2008 IEEE International Conference on Data Mining Workshops
ACM Transactions on Knowledge Discovery from Data (TKDD)
On exploiting the power of time in data mining
ACM SIGKDD Explorations Newsletter
Evaluating clustering in subspace projections of high dimensional data
Proceedings of the VLDB Endowment
On discovering moving clusters in spatio-temporal data
SSTD'05 Proceedings of the 9th international conference on Advances in Spatial and Temporal Databases
Stream-dashboard: a framework for mining, tracking and validating clusters in a data stream
Proceedings of the 1st International Workshop on Big Data, Streams and Heterogeneous Source Mining: Algorithms, Systems, Programming Models and Applications
Nesting the earth mover's distance for effective cluster tracing
Proceedings of the 25th International Conference on Scientific and Statistical Database Management
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Cluster tracing algorithms 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 designed 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.