Comparing and aggregating rankings with ties
PODS '04 Proceedings of the twenty-third ACM SIGMOD-SIGACT-SIGART symposium on Principles of database systems
Algorithms for discovering bucket orders from data
Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining
Local multidimensional scaling
Neural Networks - 2006 Special issue: Advances in self-organizing maps--WSOM'05
Efficient Clustering for Orders
ICDMW '06 Proceedings of the Sixth IEEE International Conference on Data Mining - Workshops
Curvilinear component analysis: a self-organizing neural network for nonlinear mapping of data sets
IEEE Transactions on Neural Networks
Clustering Algorithms for Chains
The Journal of Machine Learning Research
Some applications of string algorithms in human-computer interaction
Algorithms and Applications
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Partial rankings are totally ordered subsets of a set of items. They arise in different applications, such as clickstream analysis and collaborative filtering, but can be difficult to analyze with traditional data analysis techniques as they are combinatorial structures. We propose a method for creating scatterplots of sets of partial rankings by first representing them in a high-dimensional space and then applying known dimensionality reduction methods. We compare different approaches by using quantitative measures and demonstrate the methods on real data sets from different application domains. Despite their simplicity the proposed methods can produce useful visualizations that are easy to interpret.