A framework for cluster management

Authors:
Barbara Catania;Anna Maddalena
Affiliations:
Dipartimento di Informatica e Scienze dell'Informazione, Università degli Studi di Genova, Italy;Dipartimento di Informatica e Scienze dell'Informazione, Università degli Studi di Genova, Italy
Venue:
EDBT'04 Proceedings of the 2004 international conference on Current Trends in Database Technology
Year:
2004

Citing 4
Cited 0

Data clustering: a review

ACM Computing Surveys (CSUR)
Data mining: concepts and techniques

Data mining: concepts and techniques
An Extension to SQL for Mining Association Rules

Data Mining and Knowledge Discovery
MSQL: A Query Language for Database Mining

Data Mining and Knowledge Discovery

Quantified Score

Hi-index	0.00

Visualization

Abstract

To represent and manipulate data extracted from Web-server logs or applicational logs, clustering techniques can be used The generated clusters are often different in types, are generated by using different algorithms, and should be homogeneously manipulated together with other knowledge mined from data, for example association rules or decision trees The problem thus arises of using an homogeneous framework in which cluster results can be represented and manipulated, possibly together with other data mining results In this paper, we show how the pattern modeling framework presented in [5,10] can be used to this purpose.