BIRCH: an efficient data clustering method for very large databases
SIGMOD '96 Proceedings of the 1996 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
Maintaining variance and k-medians over data stream windows
Proceedings of the twenty-second ACM SIGMOD-SIGACT-SIGART symposium on Principles of database systems
FOCS '00 Proceedings of the 41st Annual Symposium on Foundations of Computer Science
Discovering Similar Multidimensional Trajectories
ICDE '02 Proceedings of the 18th International Conference on Data Engineering
Streaming-Data Algorithms for High-Quality Clustering
ICDE '02 Proceedings of the 18th International Conference on Data Engineering
Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining
ACM SIGMOD Record
Time-focused clustering of trajectories of moving objects
Journal of Intelligent Information Systems
Trajectory clustering: a partition-and-group framework
Proceedings of the 2007 ACM SIGMOD international conference on Management of data
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
A stack-based prospective spatio-temporal data analysis approach
Decision Support Systems
Density-based clustering of data streams at multiple resolutions
ACM Transactions on Knowledge Discovery from Data (TKDD)
Summarization for geographically distributed data streams
KES'10 Proceedings of the 14th international conference on Knowledge-based and intelligent information and engineering systems: Part III
On discovering moving clusters in spatio-temporal data
SSTD'05 Proceedings of the 9th international conference on Advances in Spatial and Temporal Databases
Trend cluster based kriging interpolation in sensor data networks
MSM'11 Proceedings of the 2011 international conference on Modeling and Mining Ubiquitous Social Media
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Emerging real life applications, such as environmental compliance, ecological studies and meteorology, are characterized by real-time data acquisition through remote sensor networks. The most important aspect of the sensor readings is that they comprise a space dimension and a time dimension which are both information bearing. Additionally, they usually arrive at a rapid rate in a continuous, unbounded stream. Streaming prevents us from storing all readings and performing multiple scans of the entire data set. The drift of data distribution poses the additional problem of mining patterns which may change over the time. We address these challenges for the trend cluster cluster discovery, that is, the discovery of clusters of spatially close sensors which transmit readings, whose temporal variation, called trend polyline, is similar along the time horizon of a window. We present a stream framework which segments the stream into equally-sized windows, computes online intra-window trend clusters and stores these trend clusters in a database. Trend clusters are queried offline at any time, to determine trend clusters along larger windows (i.e. windows of windows). Experiments with several streams demonstrate the effectiveness of the proposed framework in discovering accurate and relevant to human trend clusters.