Knowledge-Conscious data clustering

Authors:
Amol Ghoting;Srinivasan Parthasarathy
Affiliations:
The Ohio State University, Columbus, OH;The Ohio State University, Columbus, OH
Venue:
PKDD'06 Proceedings of the 10th European conference on Principle and Practice of Knowledge Discovery in Databases
Year:
2006

Citing 4
Cited 0

Using a knowledge cache for interactive discovery of association rules

KDD '99 Proceedings of the fifth ACM SIGKDD international conference on Knowledge discovery and data mining
Accelerating exact k-means algorithms with geometric reasoning

KDD '99 Proceedings of the fifth ACM SIGKDD international conference on Knowledge discovery and data mining
Shared State for Distributed Interactive Data Mining Applications

Distributed and Parallel Databases - Special issue: Parallel and distributed data mining
A framework for clustering evolving data streams

VLDB '03 Proceedings of the 29th international conference on Very large data bases - Volume 29

Quantified Score

Hi-index	0.00

Visualization

Abstract

We consider the problem of efficiently executing data clustering queries in a client-server setting. Extant solutions to this problem suffer from (a) a significant amount of remote I/O and (b) minimal re-use of computation between both iterations of a kMeans query, and executions of different kMeans queries. We propose to facilitate interactive kMeans clustering by employing a client-side knowledge-cache. This knowledge-cache is succinct and significantly reduces the amount of remote I/O needed during execution. Furthermore, it permits the re-use of computation, both within and between executions of the kMeans queries.