DARA : Data summarisation with feature construction

Rayner Alfred (2008) DARA : Data summarisation with feature construction. In: 2nd Asia International Conference on Modelling and Simulation (AMS), 13-15 May 2008, Kuala Lumpur.

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This paper addresses the question whether or not the descriptive accuracy of the DARA (Dynamic Aggregation of Relational Attributes) algorithm benefits from the feature construction process. This involves solving the problem of constructing a set of relevant features used to generate patterns representing records in the TF-IDF weighted frequency matrix in order to cluster these records. In this paper, feature construction will be applied to enhance the results of the data summarisation approach in learning data stored in multiple tables with high cardinality of one-to-many relations. It is expected that the predictive accuracy of a classfication problem can be improved by improving the descriptive accuracy of the data summarisation approach, provided that the summarised data is fed into the target table as one of the features considered in the classification task. © 2008 IEEE.

Item Type: Conference or Workshop Item (UNSPECIFIED)
Keyword: Agglomeration, Alpha particle spectrometers, Asset management, Chlorine compounds, Particle spectrometers Cardinality, Dynamic aggregation, Feature construction, Frequency matrix, International conferences, Learning data, Modelling and simulation, Paper addresses, Predictive accuracy Neonatal monitoring
Subjects: Q Science > QA Mathematics > QA1-939 Mathematics > QA273-280 Probabilities. Mathematical statistics
Department: SCHOOL > School of Engineering and Information Technology
Depositing User: ADMIN ADMIN
Date Deposited: 19 May 2011 16:01
Last Modified: 30 Dec 2014 14:42
URI: https://eprints.ums.edu.my/id/eprint/2651

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