Original language | English |
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Pages (from-to) | 942-953 |
Journal | IEEE Transactions on Pattern Analysis and Machine Intelligence |
Volume | 28 |
Issue number | 6 |
DOIs | |
Publication status | Published (in print/issue) - 1 Jun 2006 |
Bibliographical note
Other Details------------------------------------
There exist numerous similarity measures, but there is no generic measure that applies to different types of data. This paper presents a conceptually uniform, generic approach to measuring similarity: count common neighbourhoods. This approach has resulted in novel similarity measures for multivariate data, sequences and trees. Evaluation shows that they outperform a range of state-of-the-art measures. This work formed the basis of an EPSRC proposal on structural information retrieval. Although the proposal was not funded, it was viewed as highly innovative and ambitious. A revised proposal has been prepared and will be submitted shortly.