BIRCH: an efficient data clustering method for very large databases
SIGMOD '96 Proceedings of the 1996 ACM SIGMOD international conference on Management of data
CURE: an efficient clustering algorithm for large databases
SIGMOD '98 Proceedings of the 1998 ACM SIGMOD international conference on Management of data
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
Surfels: surface elements as rendering primitives
Proceedings of the 27th annual conference on Computer graphics and interactive techniques
Data mining: concepts and techniques
Data mining: concepts and techniques
Efficient simplification of point-sampled surfaces
Proceedings of the conference on Visualization '02
A Distribution-Based Clustering Algorithm for Mining in Large Spatial Databases
ICDE '98 Proceedings of the Fourteenth International Conference on Data Engineering
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
Geometric Approach to Clustering and Querying in Databases and Warehouses
CW '03 Proceedings of the 2003 International Conference on Cyberworlds
A Similarity-Based Robust Clustering Method
IEEE Transactions on Pattern Analysis and Machine Intelligence
Approximating Bounded, Non-Orientable Surfaces from Points
SMI '04 Proceedings of the Shape Modeling International 2004
IEEE Computer Graphics and Applications
IEEE Transactions on Knowledge and Data Engineering
Iterative shrinking method for clustering problems
Pattern Recognition
Survey of clustering algorithms
IEEE Transactions on Neural Networks
Estimation of the dynamics of two-dimensional clusters
ACC'09 Proceedings of the 2009 conference on American Control Conference
ASOD: Arbitrary shape object detection
Engineering Applications of Artificial Intelligence
Identifying hidden geospatial resources in catalogues
Proceedings of the 3rd International Conference on Web Intelligence, Mining and Semantics
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Clustering became a classical problem in databases, data warehouses, pattern recognition, artificial intelligence, and computer graphics. Applications in large spatial databases, point-based graphics, etc., give rise to new requirements for the clustering algorithms: automatic discovering of arbitrary shaped and/or non-homogeneous clusters, discovering of clusters located in low-dimensional hyperspace, detecting cluster boundaries. On that account, a new clustering and boundary detecting algorithm, ADACLUS, is proposed. It is based on the specially constructed adaptive influence function, and therefore, discovers clusters of arbitrary shapes and diverse densities, adequately captures clusters boundaries, and it is robust to noise. Normally ADACLUS performs clustering purely automatically without any preliminary parameter settings. But it also gives the user an optional possibility to set three parameters with clear meaning in order to adjust clustering for special applications. The algorithm was tested on various two-dimensional data sets, and it exhibited its effectiveness in discovering clusters of complex shapes and diverse densities. Linear complexity of the ADACLUS gives it an advantage over some well-known algorithms.