6 research outputs found
Robust and cost-effective approach for discovering action rules
The main goal of Knowledge Discovery in
Databases is to find interesting and usable patterns, meaningful
in their domain. Actionable Knowledge Discovery came to
existence as a direct respond to the need of finding more usable
patterns called actionable patterns. Traditional data mining
and algorithms are often confined to deliver frequent patterns
and come short for suggesting how to make these patterns
actionable. In this scenario the users are expected to act.
However, the users are not advised about what to do with
delivered patterns in order to make them usable. In this paper,
we present an automated approach to focus on not only creating
rules but also making the discovered rules actionable.
Up to now few works have been reported in this field which
lacking incomprehensibility to the user, overlooking the cost
and not providing rule generality. Here we attempt to present a
method to resolving these issues. In this paper CEARDM
method is proposed to discover cost-effective action rules from
data. These rules offer some cost-effective changes to
transferring low profitable instances to higher profitable ones.
We also propose an idea for improving in CEARDM method
A fuzzy method for discovering cost-effective actions from data
Data mining techniques are often confined to the delivery of frequent patterns and stop short of suggesting how to act on these patterns for business decision-making. They require human experts to post-process the discovered patterns manually. Therefore a significant need exists for techniques and tools with the ability to assist users in analyzing a large number of patterns to find usable knowledge. Action mining is one of these techniques which intelligently and automatically suggests some changes in the state of an object with the aim of gaining some profit in the corresponding domain. Up to now little research has been done in this field; in all cases continuous-valued data is handled by discretizing the associated attributes in advance or during the learning process. One inherent disadvantage in these methods is that using this sharp behavior can result in missing the optimal action. To overcome this problem this paper presents a method based on fuzzy set theory. In this paper, we concentrate on the fuzzy set based approach for the enhancement of Yang's method and present an algorithm that suggests actions which will decrease the degree to which a certain object belongs to an undesired status and increase the degree to which it belongs to a desired one. Our algorithm takes into account the fuzzy cost of actions, and further, it attempts to maximize the fuzzy net profit. The contribution of the work is in taking the output from fuzzy decision trees, and producing novel, actionable knowledge through automatic fuzzy post-processing. The performance of the proposed algorithm is compared with Yang's method using several real-life datasets taken from the UCI Machine Learning Repository. Experimental results show that the proposed algorithm outperforms Yang's method not only in finding more actions but also in finding actions with more fuzzy net profit