ThemeRiver: Visualizing Thematic Changes in Large Document Collections
IEEE Transactions on Visualization and Computer Graphics
Incremental Clustering for Mining in a Data Warehousing Environment
VLDB '98 Proceedings of the 24rd International Conference on Very Large Data Bases
Maintaining variance and k-medians over data stream windows
Proceedings of the twenty-second ACM SIGMOD-SIGACT-SIGART symposium on Principles of database systems
A framework for diagnosing changes in evolving data streams
Proceedings of the 2003 ACM SIGMOD international conference on Management of data
CURLER: finding and visualizing nonlinear correlation clusters
Proceedings of the 2005 ACM SIGMOD international conference on Management of data
Baby Names, Visualization, and Social Data Analysis
INFOVIS '05 Proceedings of the Proceedings of the 2005 IEEE Symposium on Information Visualization
The CQL continuous query language: semantic foundations and query execution
The VLDB Journal — The International Journal on Very Large Data Bases
MONIC: modeling and monitoring cluster transitions
Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining
Online clustering of parallel data streams
Data & Knowledge Engineering
Adaptive Clustering for Multiple Evolving Streams
IEEE Transactions on Knowledge and Data Engineering
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
Resource sharing in continuous sliding-window aggregates
VLDB '04 Proceedings of the Thirtieth international conference on Very large data bases - Volume 30
Mining adaptively frequent closed unlabeled rooted trees in data streams
Proceedings of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining
ICDM '08 Proceedings of the 2008 Eighth IEEE International Conference on Data Mining
Neighbor-based pattern detection for windows over streaming data
Proceedings of the 12th International Conference on Extending Database Technology: Advances in Database Technology
Mining and linking patterns across live data streams and stream archives
Proceedings of the VLDB Endowment
| Hi-index | 0.00 |
Although various mining algorithms have been proposed in the literature to efficiently compute clusters, few strides have been made to date in helping analysts to interactively explore such patterns in the stream context. We present a framework called CLUES to both computationally and visually support the process of real-time mining of density-based clusters. CLUES is composed of three major components. First, as foundation of CLUES, we develop an evolution model of density-based clusters in data streams that captures the complete spectrum of cluster evolution types across streaming windows. Second, to equip CLUES with the capability of efficiently tracking cluster evolution, we design a novel algorithm to piggy-back the evolution tracking process into the underlying cluster detection process. Third, CLUES organizes the detected clusters and their evolution interrelationships into a multidimensional pattern space - presenting clusters at different time horizons and across different abstraction levels. It provides a rich set of visualization and interaction techniques to allow the analyst to explore this multi-dimensional pattern space in real-time. Our experimental evaluation, including performance studies and a user study, using real streams from ground group movement monitoring and from stock transaction domains confirm both the efficiency and effectiveness of our proposed CLUES framework.