BIRCH: an efficient data clustering method for very large databases
SIGMOD '96 Proceedings of the 1996 ACM SIGMOD international conference on Management of data
Scalability for clustering algorithms revisited
ACM SIGKDD Explorations Newsletter
A unified and flexible framework for comparing simple and complex patterns
PKDD '04 Proceedings of the 8th European Conference on Principles and Practice of Knowledge Discovery in Databases
On Change Diagnosis in Evolving Data Streams
IEEE Transactions on Knowledge and Data Engineering
Modeling and Managing Content Changes in Text Databases
ICDE '05 Proceedings of the 21st International Conference on Data Engineering
Discovering evolutionary theme patterns from text: an exploration of temporal text mining
Proceedings of the eleventh ACM SIGKDD international conference on Knowledge discovery in data mining
A generalized framework for mining spatio-temporal patterns in scientific data
Proceedings of the eleventh ACM SIGKDD international conference on Knowledge discovery in data mining
Summarization — Compressing Data into an Informative Representation
ICDM '05 Proceedings of the Fifth IEEE International Conference on Data Mining
MONIC: modeling and monitoring cluster transitions
Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining
Density-based clustering for real-time stream data
Proceedings of the 13th 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
Knowledge Discovery from Data Streams
Knowledge Discovery from Data Streams
On discovering moving clusters in spatio-temporal data
SSTD'05 Proceedings of the 9th international conference on Advances in Spatial and Temporal Databases
Where are we going? predicting the evolution of individuals
IDA'12 Proceedings of the 11th international conference on Advances in Intelligent Data Analysis
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Monitoring and interpretation of changing patterns is a task of paramount importance for data mining applications in dynamic environments. While there is much research in adapting patterns in the presence of drift or shift, there is less research on how to maintain an overview of pattern changes over time. A major challenge lays in summarizing changes in an effective way, so that the nature of change can be understood by the user, while the demand on resources remains low. To this end, we propose FINGERPRINT, an environment for the summarization of cluster evolution. Cluster changes are captured into an "evolution graph", which is then summarized based on cluster similarity into a fingerprint of evolution by merging similar clusters. We propose a batch summarization method that traverses and summarizes the Evolution Graph as a whole, and an incremental method that is applied during the process of cluster transition discovery. We present experiments on different data streams and discuss the space reduction and information preservation achieved by the two methods.