Action-Rules: How to Increase Profit of a Company

Raś, Zbigniew W.; Wieczorkowska, Alicja

doi:10.1007/3-540-45372-5_70

Cited by 131 publications

(59 citation statements)

References 1 publication

Supporting

Mentioning

Contrasting

Order By: Relevance

“…conditional frequencies, K×L conditional frequency tables ( [Lín et. al., 2004]), Set-differs-from-set (SD) rules or for 4ft-actional rules (see [Ras & Wieczorkowska, 2000], [Rauch & Šimůnek, 2009b]). This rich syntax makes possible to involve semantically features of logical reasoning and deduction ( [Rauch, 2009]).…”

Section: Task Buildermentioning

confidence: 99%

EverMiner - towards Fully Automated KDD Process

Šimůnek¹,

Rauch²

2011

New Fundamental Technologies in Data Mining

View full text Add to dashboard Cite

Section: Task Buildermentioning

confidence: 99%

EverMiner - towards Fully Automated KDD Process

Šimůnek¹,

Rauch²

2011

New Fundamental Technologies in Data Mining

View full text Add to dashboard Cite

“…A series of works by Ras and colleagues [5], [6] mine action rules using rough set techniques. In these works the action rules are the end goal for a decision making, the predictive analytics task is not explicitly associated.…”

Section: Focusing On Sensitive Casesmentioning

confidence: 99%

Learning with Actionable Attributes: Attention -- Boundary Cases!

liobaite

Pechenizkiy

2010

2010 IEEE International Conference on Data Mining Workshops

View full text Add to dashboard Cite

Abstract-Traditional supervised learning assumes that instances are described by observable attributes. The goal is to learn to predict the labels for unseen instances. In many real world applications the values of some attributes are not only observable, but can be proactively chosen by a decision maker. Furthermore, in some of such applications the decision maker is interested not only to generate accurate predictions, but to maximize the probability of the desired outcome. For example, a direct marketing manager can choose the color of an envelope (actionable attribute), in which the offer is sent to a client, hoping that the right choice will result in a positive response with a higher probability. We study how to learn to choose the value of an actionable attribute in order to maximize the probability of a desired outcome in supervised learning settings. We emphasize that not all instances are equally sensitive to change in actions. Accurate choice of an action is essential for those instances, which are on a borderline (e.g. do not have a strong opinion). We formulate three supervised learning approaches to select the value of an actionable attribute at an instance level. We focus the learning process to the borderline cases. The potential of the underlying ideas is demonstrated with synthetic examples and a case study with a real dataset.

show abstract

“…Gradual rules are very close to action rules firstly introduced by Ras et al in [26] and extended in [34,35] in order to reclassify objects with respect to some distinguished atttribute (called a decision attribute). Intervention [11] or information changes [27] are also comparable to this framework.…”

Section: Gradual Rulesmentioning

confidence: 99%

Speed up gradual rule mining from stream data! A B-Tree and OWA-based approach

2009

View full text Add to dashboard Cite

Gradual rules allow users to be provided with rules describing the ordering correlations among attributes. Such a rule is for instance given by the higher the salary and the lower the number of cars, the higher the number of tourist travels. Previously intensively used in fuzzy command systems, these rules were manually provided to the system. More recently, they have received attention from the data mining community and methods have been defined to automatically extract and maintain gradual rules from numerical databases. However, no method has been shown to be able to handle data streams, as no method is scalable enough to manage the high rate which stream data arrive at. In this paper, we thus propose an original approach to mine data streams for gradual rules. Our method is based on B-Trees and OWA (Ordered Weighted Aggregation) operator in order to speed up the process. B-Trees are used to store already-known gradual rules in order to maintain the knowledge over time, while OWA operators provide a fast way to discard non relevant data.

show abstract

Action-Rules: How to Increase Profit of a Company

Cited by 131 publications

References 1 publication

EverMiner - towards Fully Automated KDD Process

EverMiner - towards Fully Automated KDD Process

Learning with Actionable Attributes: Attention -- Boundary Cases!

Speed up gradual rule mining from stream data! A B-Tree and OWA-based approach

Contact Info

Product

Resources

About