Clustering a DAG for CAD Databases
IEEE Transactions on Software Engineering
Cactis: a self-adaptive, concurrent implementation of an object-oriented database management system
ACM Transactions on Database Systems (TODS)
A stochastic approach for clustering in object bases
SIGMOD '91 Proceedings of the 1991 ACM SIGMOD international conference on Management of data
Effective clustering of complex objects in object-oriented databases
SIGMOD '91 Proceedings of the 1991 ACM SIGMOD international conference on Management of data
Asymmetric numerically stratified cluster methods
Asymmetric numerically stratified cluster methods
The art of computer programming, volume 2 (3rd ed.): seminumerical algorithms
The art of computer programming, volume 2 (3rd ed.): seminumerical algorithms
Effective clustering and buffering in an object-oriented dbms
Effective clustering and buffering in an object-oriented dbms
Research Frontiers in Object Technology
Information Systems Frontiers
Exploiting Data Mining Techniques for Broadcasting Data in Mobile Computing Environments
IEEE Transactions on Knowledge and Data Engineering
Incremental Clustering for Mining in a Data Warehousing Environment
VLDB '98 Proceedings of the 24rd International Conference on Very Large Data Bases
A Scalable Architecture for Autonomous Heterogeneous Database Interactions
VLDB '95 Proceedings of the 21th International Conference on Very Large Data Bases
Image-mapped data clustering: An efficient technique for clustering large data sets
Intelligent Data Analysis
Web document clustering based on web log mining
ICCOMP'06 Proceedings of the 10th WSEAS international conference on Computers
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In this study, we focus on the stability and behavior of three widely used clustering algorithms. Various correlation coefficients are computed to help us understand the sensitivity of object clustering. Surprisingly, the results indicate that there is almost no difference among any clustering approach. Furthermore, all methods appear to be near stable. These findings tend to show that the clustering algorithms are independent of the way objects are inherently clustered.