Optimal Cluster Preserving Embedding of Nonmetric Proximity Data

Optimal Cluster Preserving Embedding of Non-Metri Proximity Data

Distributional Scaling: An Algorithm for Structure-Preserving Embedding of Metric and Nonmetric Spaces

We present a novel approach for embedding general metric and nonmetric spaces into lowdimensional Euclidean spaces. As opposed to traditional multidimensional scaling techniques, which minimize the distortion of pairwise distances, our embedding algorithm seeks a low-dimensional representation of the data that preserves the structure (geometry) of the original data. The algorithm uses a hybrid ...

Indefinite Proximity Learning: A Review

Efficient learning of a data analysis task strongly depends on the data representation. Most methods rely on (symmetric) similarity or dissimilarity representations by means of metric inner products or distances, providing easy access to powerful mathematical formalisms like kernel or branch-and-bound approaches. Similarities and dissimilarities are, however, often naturally obtained by nonmetr...

Visualizing Graphs as Maps with Contiguous Regions

Relational datasets, which include clustering information, can be visualized with tools such as BubbleSets, LineSets, SOM, and GMap. The countries in SOM-based and GMap-based visualizations are fragmented, i.e., they are represented by several disconnected regions. While BubbleSets and LineSets have contiguous regions, these regions may overlap, even when the input clustering is non-overlapping...

Pairwise Data Clustering by Deterministic Annealing - Pattern Analysis and Machine Intelligence, IEEE Transactions on

Partitioning a data set and extracting hidden structure from the data arises in different application areas of pattern recognition, speech and image processing. Pairwise data clustering is a combinatorial optimization method for data grouping which extracts hidden structure from proximity data. We describe a deterministic annealing approach to pairwise clustering which shares the robustness pro...