multidimensional scaling: Using SPSS/PROXSCAL. Multidimensional scaling attempts to find the structure in a set of proximity measures between objects. This process is accomplished by assigning observations to specific locations in a conceptual low-dimensional space such that the distances between points in the space match the given (dis)similarities as closely as possible. The result is a least-squares representation of the objects in that low-dimensional space, which, in many cases, will help you to further understand your data.
Keywords for this software
References in zbMATH (referenced in 8 articles )
Showing results 1 to 8 of 8.
- Greenacre, Michael J.; Groenen, Patrick J. F.: Weighted Euclidean biplots (2016)
- Siciliano, Roberta; D’Ambrosio, Antonio; Aria, Massimo; Amodio, Sonia: Analysis of web visit histories. I: Distance-based visualization of sequence rules (2016)
- Priebe, Carey E.; Marchette, David J.; Ma, Zhiliang; Adali, Sancar: Manifold matching: joint optimization of fidelity and commensurability (2013)
- Teuerle, Marek; Żebrowski, Piotr; Magdziarz, Marcin: Multidimensional Lévy walk and its scaling limits (2012)
- Busing, Frank M. T. A.; de Rooij, Mark: Unfolding incomplete data: guidelines for unfolding row-conditional rank order data with random missings (2009)
- Köhn, Hans-Friedrich: Combinatorial individual differences scaling within the city-block metric (2006)
- Borg, Ingwer; Groenen, Patrick J. F.: Modern multidimensional scaling. Theory and applications. (2005)
- de Rooij, Mark: Distance association models for the analysis of repeated transition frequency tables. (2001)