Modular decomposition is a thoroughly investigated topic in many areas suchas switching theory, reliability theory, game theory and graph theory. Most appli-cations can be formulated in the framework of Boolean functions. In this paperwe give a uni_ed treatment of modular decomposition of...

Modular decomposition is a thoroughly investigated topic inmany areas such as switching theory, reliability theory, game theory andgraph theory. We propose an O(mn)-algorithm for the recognition of amodular set of a monotone Boolean function f with m prime implicantsand n variables. Using this...

We consider generalized monotone functions f: X -- {0,1}defined for an arbitrary binary relation = on X by theproperty x = y implies f(x) = f(y). These include thestandard monotone (or positive) Boolean functions, regular Booleanfunctions and other interesting functions as special cases. It...

Due to the present-day memory sizes, a memory-resident database has become a practical option. Consequently, new methods designed to mining in such databases are desirable. In the case of disk-resident databases, breadth-first search methods are commonly used. We propose a new algorithm, based...

Partly due to a growing interest in direct marketing, it has become an important application field for data mining. Many techniques have been applied to select the targets in commercial applications, such as statistical regression, regression trees, neural computing, fuzzy clustering and...

Direct marketing firms want to transfer their message as efficiently as possible in order to obtain a profitable long-term relationship with individual customers. Much attention has been paid to address selection of existing customers and on identifying new profitable prospects. Less attention...

This paper proposes a new algorithm for target selection. This algorithm collects all frequent patterns (equivalent to frequent item sets) in a training set. These patterns are stored e?ciently using a compact data structure called a trie. For each pattern the relative frequency of the target...

Hedonic pricing models attempt to model a relationship between object attributes and the object's price. Traditional hedonic pricing models are often parametric models that suffer from misspecification. In this paper we create these models by means of boosted CART models. The method is explained...

The brand choice problem in marketing has recently been addressed with methods from computational intelligence such as neural networks. Another class of methods from computational intelligence, the so-called ensemble methods such as boosting and stacking have never been applied to the brand...

In this report a support system for predicting end prices on eBay is proposed. The end price predictions are based on the item descriptions found in the item listings of eBay, and on some numerical item features. The system uses text mining and boosting algorithms from the field of machine...