Algorithms for clustering data
Algorithms for clustering data
Introduction to statistical pattern recognition (2nd ed.)
Introduction to statistical pattern recognition (2nd ed.)
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
Clustering Large Datasets in Arbitrary Metric Spaces
ICDE '99 Proceedings of the 15th International Conference on Data Engineering
M-FastMap: A Modified FastMap Algorithm for Visual Cluster Validation in Data Mining
PAKDD '02 Proceedings of the 6th Pacific-Asia Conference on Advances in Knowledge Discovery and Data Mining
A Prediction-Based Visual Approach for Cluster Exploration and Cluster Validation by HOV3
PKDD 2007 Proceedings of the 11th European conference on Principles and Practice of Knowledge Discovery in Databases
AI '08 Proceedings of the 21st Australasian Joint Conference on Artificial Intelligence: Advances in Artificial Intelligence
Building a Decision Cluster Forest Model to Classify High Dimensional Data with Multi-classes
ACML '09 Proceedings of the 1st Asian Conference on Machine Learning: Advances in Machine Learning
A Visual Method for High-Dimensional Data Cluster Exploration
ICONIP '09 Proceedings of the 16th International Conference on Neural Information Processing: Part II
A subspace decision cluster classifier for text classification
Expert Systems with Applications: An International Journal
An ensemble of decision cluster crotches for classification of high dimensional data
Knowledge-Based Systems
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This paper presents a visual method of cluster validation using the Fastmap algorithm. Two problems are tackled with Fastmap in the interactive process of discovering interesting clusters from real world databases. That is, (1) to verify separations of clusters created by a clustering algorithm and (2) to determine the number of clusters to be produced. They are achieved through projecting objects and clusters by Fastmap to the 2D space and visually examining the results by humans. We use a real example to show how this method has been used in discovering interesting clusters from a real data set.