Algorithms for clustering data
Algorithms for clustering data
Randomized algorithms
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
Fast algorithms for projected clustering
SIGMOD '99 Proceedings of the 1999 ACM SIGMOD international conference on Management of data
Visual classification: an interactive approach to decision tree construction
KDD '99 Proceedings of the fifth ACM SIGKDD international conference on Knowledge discovery and data mining
Finding generalized projected clusters in high dimensional spaces
SIGMOD '00 Proceedings of the 2000 ACM SIGMOD international conference on Management of data
Towards an effective cooperation of the user and the computer for classification
Proceedings of the sixth ACM SIGKDD international conference on Knowledge discovery and data mining
Interactive exploration of very large relational datasets through 3D dynamic projections
Proceedings of the sixth ACM SIGKDD international conference on Knowledge discovery and data mining
ROCK: a robust clustering algorithm for categorical attributes
Information Systems
A human-computer cooperative system for effective high dimensional clustering
Proceedings of the seventh ACM SIGKDD international conference on Knowledge discovery and data mining
Fuzzy Models and Algorithms for Pattern Recognition and Image Processing
Fuzzy Models and Algorithms for Pattern Recognition and Image Processing
Data Structures and Algorithms
Data Structures and Algorithms
HD-Eye: Visual Mining of High-Dimensional Data
IEEE Computer Graphics and Applications
A Distribution-Based Clustering Algorithm for Mining in Large Spatial Databases
ICDE '98 Proceedings of the Fourteenth 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
What Is the Nearest Neighbor in High Dimensional Spaces?
VLDB '00 Proceedings of the 26th International Conference on Very Large Data Bases
Local Dimensionality Reduction: A New Approach to Indexing High Dimensional Spaces
VLDB '00 Proceedings of the 26th International Conference on Very Large Data Bases
Efficient and Effective Clustering Methods for Spatial Data Mining
VLDB '94 Proceedings of the 20th International Conference on Very Large Data Bases
Fast Algorithms for Mining Association Rules in Large Databases
VLDB '94 Proceedings of the 20th International Conference on Very Large Data Bases
FOCS '00 Proceedings of the 41st Annual Symposium on Foundations of Computer Science
Clustering Large Datasets in Arbitrary Metric Spaces
ICDE '99 Proceedings of the 15th International Conference on Data Engineering
Collaborative Knowledge Acquisition with a Genetic Algorithm
ICTAI '97 Proceedings of the 9th International Conference on Tools with Artificial Intelligence
Xproj: a framework for projected structural clustering of xml documents
Proceedings of the 13th ACM SIGKDD international conference on Knowledge discovery and data mining
A framework for projected clustering of high dimensional data streams
VLDB '04 Proceedings of the Thirtieth international conference on Very large data bases - Volume 30
Incremental clustering of dynamic data streams using connectivity based representative points
Data & Knowledge Engineering
Connectivity based stream clustering using localised density exemplars
PAKDD'08 Proceedings of the 12th Pacific-Asia conference on Advances in knowledge discovery and data mining
Scalable Clustering for Mining Local-Correlated Clusters in High Dimensions and Large Datasets
Fundamenta Informaticae - Intelligent Data Analysis in Granular Computing
Generalized projected clustering in high-dimensional data streams
APWeb'06 Proceedings of the 8th Asia-Pacific Web conference on Frontiers of WWW Research and Development
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Abstract--Clustering is a central task in data mining applications such as customer segmentation. 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 can vary considerably in different subspaces, it is often difficult to define the notion of a cluster with the use of simple mathematical formalizations. The widely used practice of treating clustering as the exact problem of optimizing an arbitrarily chosen objective function can often lead to misleading results. In fact, the proper clustering definition may vary not only with the application and data set but also with the perceptions of the end user. This makes it difficult to separate the definition of the clustering problem from the perception of an end-user. In this paper, we propose a system which performs high-dimensional clustering by cooperation between the human and the computer. The complex task of cluster creation is accomplished through 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 for solving the clustering problem.