# Topographic Mapping of Large Dissimilarity Data Sets

Hammer B, Hasenfuss A (2010) *Neural Computation* 22(9): 2229-2284.

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Hammer, Barbara

^{UniBi}; Hasenfuss, AlexanderDepartment

Abstract

Topographic maps such as the self-organizing map (SOM) or neural gas (NG) constitute powerful data mining techniques that allow simultaneously clustering data and inferring their topological structure, such that additional features, for example, browsing, become available. Both methods have been introduced for vectorial data sets; they require a classical feature encoding of information. Often data are available in the form of pairwise distances only, such as arise from a kernel matrix, a graph, or some general dissimilarity measure. In such cases, NG and SOM cannot be applied directly. In this article, we introduce relational topographic maps as an extension of relational clustering algorithms, which offer prototype-based representations of dissimilarity data, to incorporate neighborhood structure. These methods are equivalent to the standard (vectorial) techniques if a Euclidean embedding exists, while preventing the need to explicitly compute such an embedding. Extending these techniques for the general case of non-Euclidean dissimilarities makes possible an interpretation of relational clustering as clustering in pseudo-Euclidean space. We compare the methods to well-known clustering methods for proximity data based on deterministic annealing and discuss how far convergence can be guaranteed in the general case. Relational clustering is quadratic in the number of data points, which makes the algorithms infeasible for huge data sets. We propose an approximate patch version of relational clustering that runs in linear time. The effectiveness of the methods is demonstrated in a number of examples.

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### Cite this

Hammer B, Hasenfuss A. Topographic Mapping of Large Dissimilarity Data Sets.

*Neural Computation*. 2010;22(9):2229-2284.Hammer, B., & Hasenfuss, A. (2010). Topographic Mapping of Large Dissimilarity Data Sets.

*Neural Computation*,*22*(9), 2229-2284. doi:10.1162/NECO_a_00012Hammer, B., and Hasenfuss, A. (2010). Topographic Mapping of Large Dissimilarity Data Sets.

*Neural Computation*22, 2229-2284.Hammer, B., & Hasenfuss, A., 2010. Topographic Mapping of Large Dissimilarity Data Sets.

*Neural Computation*, 22(9), p 2229-2284. B. Hammer and A. Hasenfuss, “Topographic Mapping of Large Dissimilarity Data Sets”,

*Neural Computation*, vol. 22, 2010, pp. 2229-2284. Hammer, B., Hasenfuss, A.: Topographic Mapping of Large Dissimilarity Data Sets. Neural Computation. 22, 2229-2284 (2010).

Hammer, Barbara, and Hasenfuss, Alexander. “Topographic Mapping of Large Dissimilarity Data Sets”.

*Neural Computation*22.9 (2010): 2229-2284.
This data publication is cited in the following publications:

This publication cites the following data publications:

### 4 Citations in Europe PMC

Data provided by Europe PubMed Central.

Prototype-based models in machine learning.

Biehl M, Hammer B, Villmann T.,

PMID: 26800334

Biehl M, Hammer B, Villmann T.,

*Wiley Interdiscip Rev Cogn Sci*7(2), 2016PMID: 26800334

Indefinite Proximity Learning: A Review.

Schleif FM, Tino P.,

PMID: 26313601

Schleif FM, Tino P.,

*Neural Comput*27(10), 2015PMID: 26313601

Self-Organizing Hidden Markov Model Map (SOHMMM).

Ferles C, Stafylopatis A.,

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Ferles C, Stafylopatis A.,

*Neural Netw*48(), 2013PMID: 24001407

Linear time relational prototype based learning.

Gisbrecht A, Mokbel B, Schleif FM, Zhu X, Hammer B.,

PMID: 22931439

Gisbrecht A, Mokbel B, Schleif FM, Zhu X, Hammer B.,

*Int J Neural Syst*22(5), 2012PMID: 22931439

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