Seminar on Computational Learning and
Adaptation
Conceptual Clustering on
Partitioned Data: Tree-Weaver
Jungsoon Yoo
Department of Computer Science
Middle Tennessee State University, Murfreesboro, TN
www.mtsu.edu/~csyoojp
Most Knowledge Discovery in Databases (KDD) research is concentrated
on supervised inductive learning. Conceptual clustering is an
unsupervised inductive learning technique that organizes observations
into an abstraction hierarchy without using pre-defined class values.
However, a typical conceptual clustering algorithm is not suitable for
a KDD task because of space and time constraints. Furthermore, typical
incremental and non-incremental clustering algorithms are not designed
for a partitioned data set. In this talk, I present a conceptual
clustering algorithm which works on partitioned data. The proposed
algorithm improves the clustering process by using less computation
time and less space while maintaining the clustering quality.
Date: Thurs., Nov 30
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Time: 4:15-5:30PM
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Place: Cordura 100
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