Artificial binary data scenarios

TL;DR: In this article, the authors presented a method to construct winter vacation styles on the basis of Austrian Guest Survey data, avoiding both weaknesses mentioned before, through the replicative framework provided by bagged clustering, potentially subopti mal random solutions are avoided.

TL;DR: A faster simplification of HIERDENC for hierarchical density-based clustering of categorical data, and an extension of MULIC that incorporates in the clustering process information on a software system's runtime execution are presented.

TL;DR: In this paper, a simulation environment for exploring analytical tools and joint segmentation and brand positioning strategies is tailored to comply with the perceptions-based approach to market segmentation, where the consumers' fuzzy perceptions of rivaling brands are translated into physical or functional characteristics.

TL;DR: Bagged clustering is introduced as a new exploratory approach in the field of market segmentation research which offers a few major advantages over both hierarchical and partitioning algorithms, especially when dealing with large binary data sets.

TL;DR: A new approach for using online competitive learning on binary data, where the usual Euclidean distance is replaced by binary distance measures, which take possible asymmetries of binary data into account and therefore provide a “different point of view” for looking at the data.