Optimal algorithms for approximate clustering
STOC '88 Proceedings of the twentieth annual ACM symposium on Theory of computing
Incremental clustering and dynamic information retrieval
STOC '97 Proceedings of the twenty-ninth annual ACM symposium on Theory of computing
Algorithms for facility location problems with outliers
SODA '01 Proceedings of the twelfth annual ACM-SIAM symposium on Discrete algorithms
Better streaming algorithms for clustering problems
Proceedings of the thirty-fifth annual ACM symposium on Theory of computing
Energy-Efficient Communication Protocol for Wireless Microsensor Networks
HICSS '00 Proceedings of the 33rd Hawaii International Conference on System Sciences-Volume 8 - Volume 8
HEED: A Hybrid, Energy-Efficient, Distributed Clustering Approach for Ad Hoc Sensor Networks
IEEE Transactions on Mobile Computing
INSCY: Indexing Subspace Clusters with In-Process-Removal of Redundancy
ICDM '08 Proceedings of the 2008 Eighth IEEE International Conference on Data Mining
Tight results for clustering and summarizing data streams
Proceedings of the 12th International Conference on Database Theory
Knowledge Discovery from Sensor Data
Knowledge Discovery from Sensor Data
OutRank: ranking outliers in high dimensional data
ICDEW '08 Proceedings of the 2008 IEEE 24th International Conference on Data Engineering Workshop
Precise anytime clustering of noisy sensor data with logarithmic complexity
Proceedings of the Fifth International Workshop on Knowledge Discovery from Sensor Data
Density-Based projected clustering of data streams
SUM'12 Proceedings of the 6th international conference on Scalable Uncertainty Management
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Clustering is an established data mining technique for grouping objects based on similarity. For sensor networks one aims at grouping sensor measurements in groups of similar measurements. As sensor networks have limited resources in terms of available memory and energy, a major task sensor clustering is efficient computation on sensor nodes. As a dominating energy consuming task, communication has to be reduced for a better energy efficiency. Considering memory, one has to reduce the amount of stored information on each sensor node. For in-network clustering, k-center based approaches provide k representatives out of the collected sensor measurements. We propose EDISKCO, an outlier aware incremental method for efficient detection of k-center clusters. Our novel approach is energy aware and reduces amount of required transmissions while producing high quality clustering results. In thorough experiments on synthetic and real world data sets, we show that our approach outperforms a competing technique in both clustering quality and energy efficiency. Thus, we achieve overall significantly better life times of our sensor networks.