Algorithms for clustering data
Algorithms for clustering data
Accelerating exact k-means algorithms with geometric reasoning
KDD '99 Proceedings of the fifth ACM SIGKDD international conference on Knowledge discovery and data mining
ACM Computing Surveys (CSUR)
Very fast EM-based mixture model clustering using multiresolution kd-trees
Proceedings of the 1998 conference on Advances in neural information processing systems II
Multidimensional binary search trees used for associative searching
Communications of the ACM
Clustering Algorithms
Models and issues in data stream systems
Proceedings of the twenty-first ACM SIGMOD-SIGACT-SIGART symposium on Principles of database systems
Clustering Data Streams: Theory and Practice
IEEE Transactions on Knowledge and Data Engineering
Novelty detection: a review—part 1: statistical approaches
Signal Processing
Novelty detection: a review—part 2: neural network based approaches
Signal Processing
A Survey of Outlier Detection Methodologies
Artificial Intelligence Review
A hybrid unsupervised approach for document clustering
Proceedings of the eleventh ACM SIGKDD international conference on Knowledge discovery in data mining
Data Mining: Practical Machine Learning Tools and Techniques, Second Edition (Morgan Kaufmann Series in Data Management Systems)
Modeling Emergency Response Systems
HICSS '07 Proceedings of the 40th Annual Hawaii International Conference on System Sciences
A framework for clustering evolving data streams
VLDB '03 Proceedings of the 29th international conference on Very large data bases - Volume 29
Enhanced Situational Awareness: Application of DDDAS Concepts to Emergency and Disaster Management
ICCS '07 Proceedings of the 7th international conference on Computational Science, Part I: ICCS 2007
Designing for Diagnosing: Introduction to the Special Issue on Diagnostic Work
Computer Supported Cooperative Work
Structure Correlation in Mobile Call Networks
ADMA '09 Proceedings of the 5th International Conference on Advanced Data Mining and Applications
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Mobile communication networks produce massive amounts of data which may be useful in identifying the location of an emergency situation and the area it affects. We propose a one pass clustering algorithm for quickly identifying anomalous data points. We evaluate this algorithm's ability to detect outliers in a data set and describe how such an algorithm may be used as a component of an emergency response management system.