Algorithms for clustering data
Algorithms for clustering 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
Primitives for the manipulation of general subdivisions and the computation of Voronoi
ACM Transactions on Graphics (TOG)
OPTICS: ordering points to identify the clustering structure
SIGMOD '99 Proceedings of the 1999 ACM SIGMOD international conference on Management of data
Entropy-based subspace clustering for mining numerical data
KDD '99 Proceedings of the fifth ACM SIGKDD international conference on Knowledge discovery and data mining
Clustering spatial data using random walks
Proceedings of the seventh ACM SIGKDD international conference on Knowledge discovery and data mining
A Monte Carlo algorithm for fast projective clustering
Proceedings of the 2002 ACM SIGMOD international conference on Management of data
Spatial Clustering in the Presence of Obstacles
Proceedings of the 17th International Conference on Data Engineering
Optimal Grid-Clustering: Towards Breaking the Curse of Dimensionality in High-Dimensional Clustering
VLDB '99 Proceedings of the 25th International Conference on Very Large Data Bases
Geographic Data Mining and Knowledge Discovery
Geographic Data Mining and Knowledge Discovery
Information Visualization - Special issue on coordinated and multiple views in exploratory visualization
An Efficient Mining and Clustering Algorithm for Interactive Walk-Through Traversal Patterns
WI '04 Proceedings of the 2004 IEEE/WIC/ACM International Conference on Web Intelligence
Spatial Fuzzy Clustering Using Varying Coefficients
ADMA '07 Proceedings of the 3rd international conference on Advanced Data Mining and Applications
A hybrid EM approach to spatial clustering
Computational Statistics & Data Analysis
PAKDD'08 Proceedings of the 12th Pacific-Asia conference on Advances in knowledge discovery and data mining
Exploring high-D spaces with multiform matrices and small multiples
INFOVIS'03 Proceedings of the Ninth annual IEEE conference on Information visualization
An improvement algorithm for accessing patterns through clustering in interactive VRML environments
PCM'04 Proceedings of the 5th Pacific Rim conference on Advances in Multimedia Information Processing - Volume Part III
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Clustering is one of the most important tasks for geographic knowledge discovery. However, existing clustering methods have two severe drawbacks for this purpose. First, spatial clustering methods have so far been mainly focused on searching for patterns within the spatial dimensions (usually 2D or 3D space), while more general-purpose high-dimensional (multivariate) clustering methods have very limited power in recognizing spatial patterns that involve neighbors. Secondly, existing clustering methods tend to be 'closed' and are not geared toward allowing the interaction needed to effectively support a human-led exploratory analysis. The contribution of the research includes three parts. (1) Develop an effective and efficient hierarchical spatial clustering method, which can generate a 1-D spatial cluster ordering that preserves all the hierarchical clusters. (2) Develop a density- and grid-based hierarchical subspace clustering method to effectively identify high-dimensional clusters. The spatial cluster ordering is then integrated with this subspace clustering method to effectively search multivariate spatial patterns. (3) The above two methods are implemented in a fully open and interactive manner and supported by various visualization techniques. This opens up the "black box" of the clustering process for easy understanding, steering, focusing and interpretation. At the end a working demo with US census data is presented.