In this paper, we develop a decision-theoretic frameworkfor evaluating data mining systems, which employ classification … thatarise from this point of view. The relation between the quality of a data mining system and the amount of investment that … the decision maker is willing to make is formalized. We propose two ways by which independent data mining systems can be …

Oil and gas industries archive volumes of heterogeneous data. These companies, by virtue of their diverse operations … company's business activities, the flow of data and information must be consistent and sharing among its units. In order to … improve information sharing among oil and gas company's personnel, heterogeneous data from various sources are integrated …

other financial institutions by creating membership diversity beyond the traditional common bond. Operational data stored in … data warehouses can be mined and leveraged to analyze and predict membership behavior to increase membership and asset …

Aufgabenstellung des Data Minings verstanden, welches die Aufdeckung nicht-trivialer, geschäftsrelevanter Muster und Zusammenhänge in … Bedeutung von Data Mining, um diese Daten zu analysieren und im Sinne einer Wissensentdeckung zur Verbesserung von … Managementphilosophie besteht in dem Aufbau, beziehungsweise dem Ausbau, langfristiger und profitabler Kundenbeziehungen. Data Mining …

The rapid growth in demand and supply of sophisticated data mining and analytical decision tools calls for research to … understand the value of learning, as well as how learning interacts with firms’ day-to-day marketing strategies.In this paper, we … horizon, we analytically investigate the dynamic and endogenous nature of learning processes, the interaction between learning …

ideas from treeinduction and rule learning. We examine the performance of severalGLOWER variants on two UCI data sets as … learningalgorithms will miss. In this paper, we describe and evaluate severalvariations of a new genetic learning algorithm (GLOWER) on a …

In many classification tasks training data have missing feature valuesthat can be acquired at a cost. For building …

This thesis addresses three major issues in data mining regarding feature subset selection in large dimensionality … domains, plausible reconstruction of incomplete data in cross-sectional applications, and forecasting univariate time series … stand out amongst ensemble learning algorithms: (1) the ensemble makeup is optimized automatically by SAGA; and (2) GRNN is …