Discrete-time signal processing (2nd ed.)
Discrete-time signal processing (2nd ed.)
NiagaraCQ: a scalable continuous query system for Internet databases
SIGMOD '00 Proceedings of the 2000 ACM SIGMOD international conference on Management of data
Hancock: a language for extracting signatures from data streams
Proceedings of the sixth ACM SIGKDD international conference on Knowledge discovery and data mining
Sampling from a moving window over streaming data
SODA '02 Proceedings of the thirteenth annual ACM-SIAM symposium on Discrete algorithms
Continuously adaptive continuous queries over streams
Proceedings of the 2002 ACM SIGMOD international conference on Management of data
Efficient Similarity Search In Sequence Databases
FODO '93 Proceedings of the 4th International Conference on Foundations of Data Organization and Algorithms
Maintaining variance and k-medians over data stream windows
Proceedings of the twenty-second ACM SIGMOD-SIGACT-SIGART symposium on Principles of database systems
Clustering Data Streams: Theory and Practice
IEEE Transactions on Knowledge and Data Engineering
Better streaming algorithms for clustering problems
Proceedings of the thirty-fifth annual ACM symposium on Theory of computing
FOCS '00 Proceedings of the 41st Annual Symposium on Foundations of Computer Science
Streaming-Data Algorithms for High-Quality Clustering
ICDE '02 Proceedings of the 18th International Conference on Data Engineering
Clustering binary data streams with K-means
DMKD '03 Proceedings of the 8th ACM SIGMOD workshop on Research issues in data mining and knowledge discovery
Scaling Clustering Algorithms for Massive Data Sets using Data Streams
ICDE '04 Proceedings of the 20th International Conference on Data Engineering
BRAID: stream mining through group lag correlations
Proceedings of the 2005 ACM SIGMOD international conference on Management of data
Fast window correlations over uncooperative time series
Proceedings of the eleventh ACM SIGKDD international conference on Knowledge discovery in data mining
Streaming pattern discovery in multiple time-series
VLDB '05 Proceedings of the 31st international conference on Very large data bases
Data Streaming in Telepresence Environments
IEEE Transactions on Visualization and Computer Graphics
Introduction to Data Mining, (First Edition)
Introduction to Data Mining, (First Edition)
Stream window join: tracking moving objects in sensor-network databases
SSDBM '03 Proceedings of the 15th International Conference on Scientific and Statistical Database Management
Detection and tracking of discrete phenomena in sensor-network databases
SSDBM'2005 Proceedings of the 17th international conference on Scientific and statistical database management
SlidingWindow based Multi-Join Algorithms over Distributed Data Streams
ICDE '06 Proceedings of the 22nd International Conference on Data Engineering
Online clustering of parallel data streams
Data & Knowledge Engineering
Adaptive Clustering for Multiple Evolving Streams
IEEE Transactions on Knowledge and Data Engineering
Unsupervised Clustering In Streaming Data
ICDMW '06 Proceedings of the Sixth IEEE International Conference on Data Mining - Workshops
A Distributed Algorithm for Joins in Sensor Networks
SSDBM '07 Proceedings of the 19th International Conference on Scientific and Statistical Database Management
StatStream: statistical monitoring of thousands of data streams in real time
VLDB '02 Proceedings of the 28th international conference on Very Large Data Bases
A framework for clustering evolving data streams
VLDB '03 Proceedings of the 29th international conference on Very large data bases - Volume 29
Maximizing the output rate of multi-way join queries over streaming information sources
VLDB '03 Proceedings of the 29th international conference on Very large data bases - Volume 29
Scheduling for shared window joins over data streams
VLDB '03 Proceedings of the 29th international conference on Very large data bases - Volume 29
Processing sliding window multi-joins in continuous queries over data streams
VLDB '03 Proceedings of the 29th international conference on Very large data bases - Volume 29
Adaptive, hands-off stream mining
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
Tracking clusters in evolving data streams over sliding windows
Knowledge and Information Systems
On classification and segmentation of massive audio data streams
Knowledge and Information Systems
SPARCL: an effective and efficient algorithm for mining arbitrary shape-based clusters
Knowledge and Information Systems
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Data streams and their applications appear in several fields such as physics, finance, medicine, environmental science, etc. As sensor technology improves, sensor data rates continue to increase. Consequently, analyzing data streams becomes ever more challenging. Fast online response is a must for applications that involve multiple data streams, especially when the number of data streams is large. This paper proposes an efficient clustering technique called Multi-way Grid-based join algorithm MG-join to find clusters in multiple data streams. The proposed algorithm uses a Discrete Fourier Transformation DFT to reduce the dimensionality of the streams. Each stream is represented by a point in a multi-dimensional grid in the frequency domain. The MG-join algorithm finds the different clusters in multiple data streams in the frequency domain. Moreover, this paper proposes an incremental update mechanism to avoid the recalculation of DFT coefficients when new readings arrive and thus minimizes the processing time. Experiments on synthetic data streams show that the proposed clustering technique is much faster than traditional clustering techniques and yet its accuracy is as good as that of the traditional clustering techniques. This makes the proposed technique suitable for sensors network environment where computing and power capabilities are limited.