NDLI: Cluster Based Rule Discovery Model for Enhancement of Government's Tobacco Control Strategy

Content Provider	IEEE Xplore Digital Library
Author	Huda, S. Yearwood, J. Borland, R.
Copyright Year	2010
Abstract	Discovery of interesting rules describing the behavioural patterns of smokers’ quitting intentions is an important task in the determination of an effective tobacco control strategy. In this paper, we investigate a compact and simplified rule discovery process for predicting smokers’ quitting behaviour that can provide feedback to build an scientific evidence-based adaptive tobacco control policy. Standard decision tree (SDT) based rule discovery depends on decision boundaries in the feature space which are orthogonal to the axis of the feature of a particular decision node. This may limit the ability of SDT to learn intermediate concepts for high dimensional large datasets such as tobacco control. In this paper, we propose a cluster based rule discovery model (CRDM) for generation of more compact and simplified rules for the enhancement of tobacco control policy. The cluster-based approach builds conceptual groups from which a set of decision trees (a decision forest) are constructed. Experimental results on the tobacco control data set show that decision rules from the decision forest constructed by CRDM are simpler and can predict smokers’ quitting intention more accurately than a single decision tree.
Starting Page	383
Ending Page	390
File Size	426483
Page Count	8
File Format	PDF
ISBN	9781424484843
DOI	10.1109/NSS.2010.14
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2010-09-01
Publisher Place	Australia
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Decision support systems Cluster analysis Rule discovery Error analysis Decision rule Training Tobacco control Univariate Decision Tree Clustering algorithms Training data Prediction algorithms Decision trees Multivariate decision tree
Content Type	Text
Resource Type	Article

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