Fronts propagating with curvature-dependent speed: algorithms based on Hamilton-Jacobi formulations
Journal of Computational Physics
Fundamentals of digital image processing
Fundamentals of digital image processing
Introduction to statistical pattern recognition (2nd ed.)
Introduction to statistical pattern recognition (2nd ed.)
A variational level set approach to multiphase motion
Journal of Computational Physics
Cluster analysis and mathematical programming
Mathematical Programming: Series A and B - Special issue: papers from ismp97, the 16th international symposium on mathematical programming, Lausanne EPFL
Automatic subspace clustering of high dimensional data for data mining applications
SIGMOD '98 Proceedings of the 1998 ACM SIGMOD international conference on Management of data
OPTICS: ordering points to identify the clustering structure
SIGMOD '99 Proceedings of the 1999 ACM SIGMOD international conference on Management of data
ACM Computing Surveys (CSUR)
Data mining: concepts and techniques
Data mining: concepts and techniques
Data mining with sparse grids using simplicial basis functions
Proceedings of the seventh ACM SIGKDD international conference on Knowledge discovery and data mining
Computing
Density-Based Clustering in Spatial Databases: The Algorithm GDBSCAN and Its Applications
Data Mining and Knowledge Discovery
A Min-max Cut Algorithm for Graph Partitioning and Data Clustering
ICDM '01 Proceedings of the 2001 IEEE International Conference on Data Mining
Center CLICK: A Clustering Algorithm with Applications to Gene Expression Analysis
Proceedings of the Eighth International Conference on Intelligent Systems for Molecular Biology
Efficient and Effective Clustering Methods for Spatial Data Mining
VLDB '94 Proceedings of the 20th International Conference on Very Large Data Bases
STING: A Statistical Information Grid Approach to Spatial Data Mining
VLDB '97 Proceedings of the 23rd International Conference on Very Large Data Bases
WaveCluster: a wavelet-based clustering approach for spatial data in very large databases
The VLDB Journal — The International Journal on Very Large Data Bases
The Journal of Machine Learning Research
Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
Geometric Partial Differential Equations and Image Analysis
Geometric Partial Differential Equations and Image Analysis
Parallelisation of sparse grids for large scale data analysis
ICCS'03 Proceedings of the 2003 international conference on Computational science: PartIII
Cluster aggregate inequality and multi-level hierarchical clustering
PKDD'05 Proceedings of the 9th European conference on Principles and Practice of Knowledge Discovery in Databases
GAPS: A clustering method using a new point symmetry-based distance measure
Pattern Recognition
A Clustering Algorithm Based on Adaptive Subcluster Merging
CAI '07 Proceedings of the 20th conference of the Canadian Society for Computational Studies of Intelligence on Advances in Artificial Intelligence
Level Learning Set: A Novel Classifier Based on Active Contour Models
ECML '07 Proceedings of the 18th European conference on Machine Learning
Density-based clustering using graphics processors
Proceedings of the 18th ACM conference on Information and knowledge management
Iterative Bayesian fuzzy clustering toward flexible icon-based assistive software for the disabled
Information Sciences: an International Journal
Data clustering using a modified Kuwahara filter
IJCNN'09 Proceedings of the 2009 international joint conference on Neural Networks
Dimension and coverage of multiple-models structures using clustering techniques
MIC '08 Proceedings of the 27th IASTED International Conference on Modelling, Identification and Control
Classification Using Geometric Level Sets
The Journal of Machine Learning Research
Scalable Clustering for Mining Local-Correlated Clusters in High Dimensions and Large Datasets
Fundamenta Informaticae - Intelligent Data Analysis in Granular Computing
A new clustering approach on the basis of dynamical neural field
Neural Computation
Classification of polarimetric SAR data based on multidimensional watershed clustering
ADMA'06 Proceedings of the Second international conference on Advanced Data Mining and Applications
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Density-based clustering has the advantages for 1) allowing arbitrary shape of cluster and 2) not requiring the number of clusters as input. However, when clusters touch each other, both the cluster centers and cluster boundaries (as the peaks and valleys of the density distribution) become fuzzy and difficult to determine. We introduce the notion of cluster intensity function (CIF) which captures the important characteristics of clusters. When clusters are well-separated, CIFs are similar to density functions. But, when clusters become closed to each other, CIFs still clearly reveal cluster centers, cluster boundaries, and degree of membership of each data point to the cluster that it belongs. Clustering through bump hunting and valley seeking based on these functions are more robust than that based on density functions obtained by kernel density estimation, which are often oscillatory or oversmoothed. These problems of kernel density estimation are resolved using Level Set Methods and related techniques. Comparisons with two existing density-based methods, valley seeking and DBSCAN, are presented which illustrate the advantages of our approach.