Konstanz, Univ., Diplomarb., 2001.

Stuttgart, Univ., Diplomarbeit, 2006.

Zürich, Techn. Hochsch., Diss., 2007.

The search for clusters in empirical data is an important and often encountered research problem. Numerous algorithms exist that are able to render groups of objects or individuals. Of course each algorithm has its strengths and weaknesses. In order to identify these crucial points artificial data was generated - based primarily on experience with structures of empirical data - and used as benchmark for evaluating the results of numerous cluster algorithms. This work is an addition to SFB Working Paper No. 7 where hard competitive learning (HCL), neural gas (NGAS), k-means and self organizing maps (SOMs) were compared. Since the artificial data scenarios and the evaluation criteria used remained the same, they are not explained in this work, where the results of five additional algorithms are evaluated. (author's abstract) / Series: Working Papers SFB "Adaptive Information Systems and Modelling in Economics and Management Science"

Social scientists confronted with the problem of segmenting individuals into plausible subgroups usually encounter two main problems: First: there is very little indication about the correct choice of the number of clusters to search for. Second: different cluster algorithms and even multiple replications of the same algorithm result in different solutions due to random initializations and stochastic learning methods. In the worst case numerous solutions are found which all seem plausible as far as interpretation is concerned. The consequence is, that in the end clusters are postulated that are in fact "chosen" by the researcher, as he or she makes decisions on the number of clusters and the solution chosen as the "final" one. In this paper we concentrate on the power and stability of several popular clustering algorithms under the condition that the correct number of clusters is known. Artificial data sets modeled to mimic typical situations from tourism marketing are constructed. The structure of these data sets is described in several scenarios, and artificial binary data are generated accordingly. These data, ranging from very simple to more complex, real-data-like structures, enable us to systematically analyze the "behavior" of the cluster methods. Section 3 gives an overview of all cluster methods under investigation. Section 4 describes our experimental results, comparing first all scenarios and then all cluster methods. To accomplish this task, several evaluation criteria for cluster methods are proposed. Finally: Sections 5 and 6 draw some conclusions and give an outlook on future research. (author's abstract) / Series: Working Papers SFB "Adaptive Information Systems and Modelling in Economics and Management Science"

Zugl.: Berlin, Freie Universiẗat, Diss., 2009.

Binary survey data from the Austrian National Guest Survey conducted in the summer season of 1997 were used to identify behavioral market segments on the basis of vacation activity information. Bagged clustering overcomes a number of difficulties typically encountered when partitioning large binary data sets: The partitions have greater structural stability over repetitions of the algorithm and the question of the "correct" number of clusters is less important because of the hierarchical step of the cluster analysis. Finally, the bootstrap part of the algorithm provides means for assessing and visualizing segment stability for each input variable. (author's abstract) / Series: Report Series SFB "Adaptive Information Systems and Modelling in Economics and Management Science"

Zugl.: Hannover, Universiẗat, Diss., 2007.

Zugl.: Braunschweig, Techn. Univ., Diss., 2005

Zugl.: Karlsruhe, Univ., Diss., 2005