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The automatic induction of classification rules from examples is an important technique used in data mining. One of the problems encountered is the overfitting of rules to training data. In some cases this can lead to an excessively large number of rules, many of which have very little predictive value for unseen data. This paper is concerned with the(More)
—Pocket Data Mining PDM is our new term describing collaborative mining of streaming data in mobile and distributed computing environments. With sheer amounts of data streams are now available for subscription on our smart mobile phones, the potential of using this data for decision making using data stream mining techniques has now been achievable owing to(More)
The Prism family of algorithms induces modular classification rules in contrast to the Top Down Induction of Decision Trees (TDIDT) approach which induces classification rules in the intermediate form of a tree structure. Both approaches achieve a comparable classification accuracy. However in some cases Prism outperforms TDIDT. For both approaches(More)
The Prism family of algorithms induces modular classification rules which, in contrast to decision tree induction algorithms, do not necessarily fit together into a decision tree structure. Classifiers induced by Prism algorithms achieve a comparable accuracy compared with decision trees and in some cases even out-perform decision trees. Both kinds of(More)
A brief overview of the history of the development of decision tree induction algorithms is followed by a review of techniques for dealing with missing attribute values in the operation of these methods. The technique of dynamic path generation is described in the context of tree-based classiication methods. The waste of data which can result from casewise(More)