The increasing awareness of data mining technology, along with the attendant increase in the capturing, warehousing, and utilization of historical data to support evidence-based decision making, is leading many organizations to recognize that the effective use of data is the key element in the...

In this paper, we develop a decision-theoretic frameworkfor evaluating data mining systems, which employ classification methods, in terms of their utility in decision-making. The decision-theoretic model provides an economic perspective on the value of â extracted knowledge,â in terms of its...

In der vorliegenden Dissertation werden zwei neue Data Mining Verfahren und Metho-den zur Extraktion von qualitätsrelevantem Wissen vorgestellt und mit Anwendungen aus der Automobilbranche evaluiert. Diese neuartigen Verfahren erlauben es, in Abhän-gigkeit der zugrunde liegenden Datentypen,...

This thesis utilizes recommender systems to aid the user in contributing to the Social Semantic Web. In this work, we propose a framework that maps domain properties to recommendation technologies. Next, we develop novel recommendation algorithms for improving personalized tag recommendation and...

This paper proposes the use of an integrated information retrieval framework (IIRF) to facilitate the collection of critical information of a manufacturer’s supply network. It explores the requirements for a sustainable survival of manufacturers by aligning information among the entities...

Oil and gas industries archive volumes of heterogeneous data. These companies, by virtue of their diverse operations, comprise of complicated organizational structure. The nature of organizational set-up with several operational units, often results with communication barriers among operational...

Prediction in financial domains is notoriously difficult for a number ofreasons. First, theories tend to be weak or non-existent, which makesproblem formulation open ended by forcing us to consider a large numberof independent variables and thereby increasing the dimensionality ofthe search...

In many classification tasks training data have missing feature valuesthat can be acquired at a cost. For building accurate predictive models,acquiring all missing values is often prohibitively expensive orunnecessary, while acquiring a random subset of feature values may notbe most effective....