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Getting Things in Order: An Introduction to the R Package seriation

Seriation, i.e., finding a suitable linear order for a set of objects given data and a loss or merit function, is a basic problem in data analysis. Caused by the problem's combinatorial nature, it is hard to solve for all but very small sets. Nevertheless, both exact solution methods and heuristics are available. In this paper we present the package seriation which provides an infrastructure for seriation with R. The infrastructure comprises data structures to represent linear orders as permutation vectors, a wide array of seriation methods using a consistent interface, a method to calculate the value of various loss and merit functions, and several visualization techniques which build on seriation. To illustrate how easily the package can be applied for a variety of applications, a comprehensive collection of examples is presented.

Is thinking worthwhile? A comparison of corporate segment choice strategies.

The field of strategic marketing has long been identified as fruitful ground for gaining competitive advantage. Ever since the market segmentation concept was introduced in the late sixties, research interest steadily increased, covering issues as e.g. which fundamental segmentation strategy is most appropriate, in which ways can segments be identified or constructed, which algorithm provides optimal data-driven segmentation solutions, which number of segments should be constructed etc.. Interestingly, the issue of segment evaluation and choice has not been emphasised very strongly in the past, although this is of primary interest as soon as it comes to practical implementation. This article tries to fill this gap in an experimental manner: the consequences of different corporate segment choice strategies based on different segment evaluation criteria are investigated under different environmental conditions formalised in a complex artificial consumer market. The results indicate that complex decision models for segment choice do not turn out to be superior in general. Both mass marketers and firms concentrating on particular segments based on an a priori logic can be just as successful under "favourable" market conditions, the most influential condition being the available advertising budget. (author's abstract)Series: Working Papers SFB "Adaptive Information Systems and Modelling in Economics and Management Science

Association of CLEC16A with human common variable immunodeficiency disorder and role in murine B cells

Common variable immunodeficiency disorder (CVID) is the most common symptomatic primary immunodeficiency in adults, characterized by B-cell abnormalities and inadequate antibody response. CVID patients have considerable autoimmune comorbidity and we therefore hypothesized that genetic susceptibility to CVID may overlap with autoimmune disorders. Here, in the largest genetic study performed in CVID to date, we compare 778 CVID cases with 10, 999 controls across 123, 127 single-nucleotide polymorphisms (SNPs) on the Immunochip. We identify the first non-HLA genome-wide significant risk locus at CLEC16A (rs17806056, P = 2.0 x 10(-9)) and confirm the previously reported human leukocyte antigen (HLA) associations on chromosome 6p21 (rs1049225, P = 4.8 x 10(-16)). Clec16a knockdown (KD) mice showed reduced number of B cells and elevated IgM levels compared with controls, suggesting that CLEC16A may be involved in immune regulatory pathways of relevance to CVID. In conclusion, the CLEC16A associations in CVID represent the first robust evidence of non-HLA associations in this immunodeficiency condition

Open-Source Machine Learning: R Meets Weka

Two of the prime open-source environments available for machine/statistical learning in data mining and knowledge discovery are the software packages Weka and R which have emerged from the machine learning and statistics communities, respectively. To make the different sets of tools from both environments available in a single unified system, an R package RWeka is suggested which interfaces Weka's functionality to R. With only a thin layer of (mostly R) code, a set of general interface generators is provided which can set up interface functions with the usual "R look and feel", re-using Weka's standardized interface of learner classes (including classifiers, clusterers, associators, filters, loaders, savers, and stemmers) with associated methods.Series: Research Report Series / Department of Statistics and Mathematic

Getting Things in Order: An Introduction to the R package seriation

Seriation, i.e., finding a linear order for a set of objects given data and a loss or merit function, is a basic problem in data analysis. Caused by the problem's combinatorial nature, it is hard to solve for all but very small sets. Nevertheless, both exact solution methods and heuristics are available. In this paper we present the package seriation which provides the infrastructure for seriation with R. The infrastructure comprises data structures to represent linear orders as permutation vectors, a wide array of seriation methods using a consistent interface, a method to calculate the value of various loss and merit functions, and several visualization techniques which build on seriation. To illustrate how easily the package can be applied for a variety of applications, a comprehensive collection of examples is presented.Series: Research Report Series / Department of Statistics and Mathematic

Spherical k-Means Clustering

Clustering text documents is a fundamental task in modern data analysis, requiring approaches which perform well both in terms of solution quality and computational efficiency. Spherical k-means clustering is one approach to address both issues, employing cosine dissimilarities to perform prototype-based partitioning of term weight representations of the documents. This paper presents the theory underlying the standard spherical k-means problem and suitable extensions, and introduces the R extension package skmeans which provides a computational environment for spherical k-means clustering featuring several solvers: a fixed-point and genetic algorithm, and interfaces to two external solvers (CLUTO and Gmeans). Performance of these solvers is investigated by means of a large scale benchmark experiment. (authors' abstract

SIMENV: A dynamic simulation environment for heterogeneous agents

We introduce a generic simulation framework suitable for agent-based simulations featuring the support of heterogeneous agents, hierarchical scheduling, and flexible specification of design parameters. One key aspect of this framework is the design specification: we use a format based on the Extensible Markup Language (XML) that is simple-structured yet still enables the design of flexible models, with the possibility of varying both agent population and parameterization. Further, the tool allows the defi- nition of communication channels to single or groups of agents, and handles the information exchange. Also, both (groups of) agents and communications channels can be added and removed at runtime by the agents, thus allowing dynamic settings with the agent population and/or communication structures varying during the simulation time. A second issue in agent-based simulations, especially when readymade components are used, is the heterogeneity arising from both the agents' implementations and the underlying platforms: for this, we introduce a wrapper technique for mapping the functionality of agents living in an interpreter-based environment to a standardized JAVA interface, thus facilitating the task for any control mechanism (like a simulation manager). Again, this mapping is made by an XML-based definition format. (author's abstract)Series: Working Papers SFB "Adaptive Information Systems and Modelling in Economics and Management Science

Learning by simulation. Computer simulations for strategic marketing decision support in tourism.

This paper describes the use of corporate decision and strategy simulations as a decision-support instrument under varying market conditions in the tourism industry. It goes on to illustrate this use of simulations with an experiment which investigates how successful different market segmentation approaches are in destination management. The experiment assumes a competitive environment and various cycle-length conditions with regard to budget and strategic planning. Computer simulations prove to be a useful management tool, allowing customized experiments which provide insight into the functioning of the market and therefore represent an interesting tool for managerial decision support. The main drawback is the initial setup of a customized computer simulation, which is time-consuming and involves defining parameters with great care in order to represent the actual market environment and to avoid excessive complexity in testing cause-effect-relationships. (author's abstract)Series: Report Series SFB "Adaptive Information Systems and Modelling in Economics and Management Science