Introduction to statistical pattern recognition (2nd ed.)
Introduction to statistical pattern recognition (2nd ed.)
OPTICS: ordering points to identify the clustering structure
SIGMOD '99 Proceedings of the 1999 ACM SIGMOD international conference on Management of data
A view of the EM algorithm that justifies incremental, sparse, and other variants
Learning in graphical models
Fast density estimation using CF-kernel for very large databases
KDD '99 Proceedings of the fifth ACM SIGKDD international conference on Knowledge discovery and data mining
Estimation of the number of clusters and influences zones
Pattern Recognition Letters
Fuzzy Models and Algorithms for Pattern Recognition and Image Processing
Fuzzy Models and Algorithms for Pattern Recognition and Image Processing
Density-Based Clustering in Spatial Databases: The Algorithm GDBSCAN and Its Applications
Data Mining and Knowledge Discovery
On supervised density estimation techniques and their application to spatial data mining
Proceedings of the 15th annual ACM international symposium on Advances in geographic information systems
Density-based clustering using graphics processors
Proceedings of the 18th ACM conference on Information and knowledge management
SPARCL: an effective and efficient algorithm for mining arbitrary shape-based clusters
Knowledge and Information Systems
Automatic parameter determination in subspace clustering with gravitation function
Proceedings of the Fourteenth International Database Engineering & Applications Symposium
Private memoirs of a smart meter
Proceedings of the 2nd ACM Workshop on Embedded Sensing Systems for Energy-Efficiency in Building
Mining spatial trajectories using non-parametric density functions
MLDM'11 Proceedings of the 7th international conference on Machine learning and data mining in pattern recognition
Event detection and scene attraction by very simple contextual cues
J-MRE '11 Proceedings of the 2011 joint ACM workshop on Modeling and representing events
Indexing media by personal events
Proceedings of the 2nd ACM International Conference on Multimedia Retrieval
Clustering based on density estimation with sparse grids
KI'12 Proceedings of the 35th Annual German conference on Advances in Artificial Intelligence
Proceedings of the 1st ACM international workshop on Multimedia indexing and information retrieval for healthcare
Fast parameterless density-based clustering via random projections
Proceedings of the 22nd ACM international conference on Conference on information & knowledge management
Automatic player behavior analysis system using trajectory data in a massive multiplayer online game
Multimedia Tools and Applications
Subspace clustering of high-dimensional data: an evolutionary approach
Applied Computational Intelligence and Soft Computing
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The Denclue algorithm employs a cluster model based on kernel density estimation. A cluster is defined by a local maximum of the estimated density function. Data points are assigned to clusters by hill climbing, i.e. points going to the same local maximum are put into the same cluster. A disadvantage of Denclue 1.0 is, that the used hill climbing may make unnecessary small steps in the beginning and never converges exactly to the maximum, it just comes close. We introduce a new hill climbing procedure for Gaussian kernels, which adjusts the step size automatically at no extra costs. We prove that the procedure converges exactly towards a local maximum by reducing it to a special case of the expectation maximization algorithm. We show experimentally that the new procedure needs much less iterations and can be accelerated by sampling based methods with sacrificing only a small amount of accuracy.