Algorithms for clustering data
Algorithms for clustering data
Advances in knowledge discovery and data mining
Advances in knowledge discovery and data mining
OPTICS: ordering points to identify the clustering structure
SIGMOD '99 Proceedings of the 1999 ACM SIGMOD international conference on Management of data
Scale-Space Theory in Computer Vision
Scale-Space Theory in Computer Vision
Clustering Algorithms
Density-Based Multiscale Data Condensation
IEEE Transactions on Pattern Analysis and Machine Intelligence
A Least Biased Fuzzy Clustering Method
IEEE Transactions on Pattern Analysis and Machine Intelligence
Significance Tests for Patterns in Continuous Data
ICDM '01 Proceedings of the 2001 IEEE International Conference on Data Mining
Handling Feature Ambiguity in Knowledge Discovery from Time Series
DS '02 Proceedings of the 5th International Conference on Discovery Science
Pattern Detection and Discovery
Proceedings of the ESF Exploratory Workshop on Pattern Detection and Discovery
Determining Hit Rate in Pattern Search
Proceedings of the ESF Exploratory Workshop on Pattern Detection and Discovery
Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
Matching Partitions over Time to Reliably Capture Local Clusters in Noisy Domains
PKDD 2007 Proceedings of the 11th European conference on Principles and Practice of Knowledge Discovery in Databases
How Much True Structure Has Been Discovered?
MLDM '09 Proceedings of the 6th International Conference on Machine Learning and Data Mining in Pattern Recognition
Cluster-grouping: from subgroup discovery to clustering
Machine Learning
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The starting point of this work is the definition of local pattern detection given in [10] as the unsupervised detection of local regions with anomalously high data density, which represent real underlying phenomena. We discuss some aspects of this definition and examine the differences between clustering and pattern detection (if any), before we investigate how to utilize clustering algorithms for pattern detection. A modification of an existing clustering algorithm is proposed to identify local patterns that are flagged as being significant according to a statistical test.