Algorithms for clustering data
Algorithms for clustering data
C4.5: programs for machine learning
C4.5: programs for machine learning
Efficiently supporting ad hoc queries in large datasets of time sequences
SIGMOD '97 Proceedings of the 1997 ACM SIGMOD international conference on Management of data
From data mining to knowledge discovery: an overview
Advances in knowledge discovery and data mining
Data mining: concepts and techniques
Data mining: concepts and techniques
Information visualization in data mining and knowledge discovery
Information visualization in data mining and knowledge discovery
Numerical Recipes in C: The Art of Scientific Computing
Numerical Recipes in C: The Art of Scientific Computing
Brief Application Description; Visual Data Mining: Recognizing Telephone Calling Fraud
Data Mining and Knowledge Discovery
Visual Data Mining In Atmospheric Science Data
Data Mining and Knowledge Discovery
On Clustering Validation Techniques
Journal of Intelligent Information Systems
Visualization Techniques for Mining Large Databases: A Comparison
IEEE Transactions on Knowledge and Data Engineering
Quantifiable data mining using ratio rules
The VLDB Journal — The International Journal on Very Large Data Bases
Pattern Recognition, Third Edition
Pattern Recognition, Third Edition
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In this paper we describe an interactive, visual knowledge discovery tool for analyzing numerical data sets. The tool combines a visual clustering method, to hypothesize meaningful structures in the data, and a classification machine learning algorithm, to validate the hypothe-psized structures. A two-dimensional representation of the available data allows a user to partition the search space by choosing shape or density according to criteria he deems optimal. A partition can be composed by regions populated according to some arbitrary form, not necessarily spherical. The accuracy of clustering results can be validated by using a decision tree classifier, included in the mining tool.