Cluster based rule discovery model for enhancement of government's tobacco control strategy
- Authors: Huda, Shamsul , Yearwood, John , Borland, Ron
- Date: 2010
- Type: Text , Conference proceedings
- Full Text:
- Description: 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 clusterbased 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. © 2010 IEEE.
Smokers' characteristics and cluster based quitting rule discovery model for enhancement of government's tobacco control systems
- Authors: Huda, Shamsul , Yearwood, John , Borland, Ron
- Date: 2010
- Type: Text , Conference paper
- Relation: Proceedings of the 14th Pacific Asia Conference on Information Systems (PACIS 2010)
- Full Text:
- Reviewed:
- Description: Discovery of cluster characteristics and interesting rules describing smokers' clusters and the behavioural patterns of smoker's quitting intentions is an important task in the development of an effective tobacco control systems. In this paper, we attempt to determine the characteristics smokers' cluster and simplified rule for predicting smokers' quitting behaviour that can provide feedback to build a scientific evidence-based adaptive tobacco control systems. Standard clustering algorithm groups the data based on there inherent pattern. "From abstract"
- Description: Discovery of cluster characteristics and interesting rules describing smokers' clusters and the behavioural patterns of smoker's quiiting intentios is an important task in the development of an effective tobacco control systems. In this paper, we attempt to determine the characteristics smokers' cluster and simplified rule for predicting smokers' quitting behaviour that can provide feedback to build a scientific evidence-based adaptive tobacco control systems. Standard clustering algorithm groups the data based on there inherent pattern. "From abstract"