Silhouettes: a graphical aid to the interpretation and validation of cluster analysis
Journal of Computational and Applied Mathematics
How many clusters are best?—an experiment
Pattern Recognition
Algorithms for clustering data
Algorithms for clustering data
Introduction to statistical pattern recognition (2nd ed.)
Introduction to statistical pattern recognition (2nd ed.)
Identifying genuine clusters in a classification
Computational Statistics & Data Analysis
SIGMOD '95 Proceedings of the 1995 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
BIRCH: A New Data Clustering Algorithm and Its Applications
Data Mining and Knowledge Discovery
Extensions to the k-Means Algorithm for Clustering Large Data Sets with Categorical Values
Data Mining and Knowledge Discovery
Efficient and Effective Clustering Methods for Spatial Data Mining
VLDB '94 Proceedings of the 20th International Conference on Very Large Data Bases
A Visual Method of Cluster Validation with Fastmap
PADKK '00 Proceedings of the 4th Pacific-Asia Conference on Knowledge Discovery and Data Mining, Current Issues and New Applications
An Empirical Study on the Visual Cluster Validation Method with Fastmap
DASFAA '01 Proceedings of the 7th International Conference on Database Systems for Advanced Applications
Clustering Large Datasets in Arbitrary Metric Spaces
ICDE '99 Proceedings of the 15th International Conference on Data Engineering
Cluster Analysis
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This paper presents M-FastMap, a modified FastMap algorithm for visual cluster validation in data mining. In the visual cluster validation with FastMap, clusters are first generated with a clustering algorithm from a database. Then, the FastMap algorithm is used to project the clusters onto a 2-dimensional (2D) or 3-dimensional (3D) space and the clusters are visualized with different colors and/or symbols on a 2D (or 3D) display. From the display a human can visually examine the separation of clusters. This method follows the principle that if a cluster is separate from others in the projected 2D (or 3D) space, it is also separate from others in the original high dimensional space (the opposite is not true). The modified FastMap algorithm improves the quality of visual cluster validation by optimizing the separation of clusters on the 2D or (3D) space in the selection of pivot objects (or projection axis). The comparison study has shown that the modified FastMap algorithm can produce better visualization results than the original FastMap algorithm.