Algorithms for clustering data
Algorithms for clustering data
The R*-tree: an efficient and robust access method for points and rectangles
SIGMOD '90 Proceedings of the 1990 ACM SIGMOD international conference on Management of data
SIGMOD '95 Proceedings of the 1995 ACM SIGMOD international conference on Management of data
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
R-trees: a dynamic index structure for spatial searching
SIGMOD '84 Proceedings of the 1984 ACM SIGMOD international conference on Management of data
Efficient and Effective Clustering Methods for Spatial Data Mining
VLDB '94 Proceedings of the 20th International Conference on Very Large Data Bases
A Study of Spatial Clustering techniques
DEXA '94 Proceedings of the 5th International Conference on Database and Expert Systems Applications
Guiding knowledge discovery through interactive data mining
Managing data mining technologies in organizations
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Data Mining has been a hot topic in computer science. Many researchers have been putting lots of efforts on how to extract explicit knowledge from large databases. Among the problems in data mining, finding useful patterns in large databases has attracted lots of interest in recent years. However, like other data mining algorithms, most of the proposed clustering algorithms are suffering from the same demerit: lack of user interaction and exploration. In this paper, a new algorithm called Interactive Data Analysis on Numeric-data : IDAN is being introduced. IDAN is good in discovering clustering patterns from numeric data. This algorithm is incremental and providing more user interaction in the mining process. At the same time, it allows the user to explore the rules or clusters being found when integrated with a visualizer.