Mining quantitative association rules in large relational tables
SIGMOD '96 Proceedings of the 1996 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
Fast algorithms for projected clustering
SIGMOD '99 Proceedings of the 1999 ACM SIGMOD international conference on Management of data
Finding generalized projected clusters in high dimensional spaces
SIGMOD '00 Proceedings of the 2000 ACM SIGMOD international conference on Management of data
HD-Eye: Visual Mining of High-Dimensional Data
IEEE Computer Graphics and Applications
Towards effective and interpretable data mining by visual interaction
ACM SIGKDD Explorations Newsletter
Why so many clustering algorithms: a position paper
ACM SIGKDD Explorations Newsletter
Visualizing changes in the structure of data for exploratory feature selection
Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining
A Human-Computer Interactive Method for Projected Clustering
IEEE Transactions on Knowledge and Data Engineering
On improved projection techniques to support visual exploration of multidimensional data sets
Information Visualization - Special issue on coordinated and multiple views in exploratory visualization
Projective Clustering by Histograms
IEEE Transactions on Knowledge and Data Engineering
On the use of Human-Computer Interaction for Projected Nearest Neighbor Search
Data Mining and Knowledge Discovery
Toward Exploratory Test-Instance-Centered Diagnosis in High-Dimensional Classification
IEEE Transactions on Knowledge and Data Engineering
Efficient mining of understandable patterns from multivariate interval time series
Data Mining and Knowledge Discovery
Indexing for multipoint interactive similarity retrieval in iconic spatial image databases
Journal of Visual Languages and Computing
GAM: a guidance enabled association mining environment
International Journal of Business Intelligence and Data Mining
Query result clustering for object-level search
Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining
Mobile software agents for location-based systems
NODe'02 Proceedings of the NODe 2002 agent-related conference on Agent technologies, infrastructures, tools, and applications for E-services
Hybrid entity clustering using crowds and data
The VLDB Journal — The International Journal on Very Large Data Bases
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High dimensional data has always been a challenge for clustering algorithms because of the inherent sparsity of the points. Therefore, techniques have recently been proposed to find clusters in hidden subspaces of the data. However, since the behavior of the data may vary considerably in different subspaces, it is often difficult to define the notion of a cluster with the use of simple mathematical formalizations. In fact, the meaningfulness and definition of a cluster is best characterized with the use of human intuition. In this paper, we propose a system which performs high dimensional clustering by effective cooperation between the human and the computer. The complex task of cluster creation is accomplished by a combination of human intuition and the computational support provided by the computer. The result is a system which leverages the best abilities of both the human and the computer in order to create very meaningful sets of clusters in high dimensionality.