SimUSF: an efficient and effective similarity measure that is invariant to violations of the interval scale assumption
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Similarity measures are central to many machine learning algorithms. There are many different similarity measures, each catering for different applications and data requirements. Most similarity measures used with numerical data assume that the attributes are interval scale. In the interval scale, it is assumed that a unit difference has the same meaning irrespective of the magnitudes of the values separated. When this assumption is violated, accuracy may be reduced. Our experiments show that removing the interval scale assumption by transforming data to ranks can improve the accuracy of distance-based similarity measures on some tasks. However the rank transform has high time and storage overheads. In this paper, we introduce an efficient similarity measure which does not consider the magnitudes of inter-instance distances. We compare the new similarity measure with popular similarity measures in two applications: DBScan clustering and content based multimedia information retrieval with real world datasets and different transform functions. The results show that the proposed similarity measure provides good performance on a range of tasks and is invariant to violations of the interval scale assumption.
KeywordsSimilarity measure Interval scale Clustering CBMIR
We are grateful to Francois Petitjean for valuable feedback and suggestions. This research has been supported by the Australian Research Council under Grant DP140100087.
- Conover WJ (1980) Practical nonparametric statistics. Wiley, New YorkGoogle Scholar
- Ester M, Kriegel HP, Sander J, Xu X (1996) A density-based algorithm for discovering clusters in large spatial databases with noise. In: Proceedings of the second ACM international conference on knowledge discovery and data mining, pp 226–231Google Scholar
- Giacinto G, Roli F (2005) Instance-based relevance feedback for image retrieval. Adv Neural Inf Process Syst 17:489–496Google Scholar
- He J, Li M, Zhang HJ, Tong H, Zhang C (2004) Manifold-ranking based image retrieval. In: Proceedings of the 12th annual ACM international conference on multimedia, MULTIMEDIA ’04, ACM, New York, pp 9–16Google Scholar
- Lichman M (2014) UCI machine learning repository. http://archive.ics.uci.edu/ml. Accessed 22 Oct 2014
- Osborne J (2002) Notes on the use of data transformations. Pract Assess Res Eval 8(6):1–8Google Scholar
- Osborne JW (2010) Improving your data transformations: applying the box-cox transformation. Pract Assess Res Eval 15(12):1–9Google Scholar
- Rocchio JJ (1971) Relevance feedback in information retrieval. In: Salton G (ed) The SMART retrieval system: experiments in automatic document processing. Prentice-Hall, Englewood Cliffs, pp 313–323Google Scholar
- SIGKDD (2015) 2014 SIGKDD test of time award winners. http://www.kdd.org/awards/view/2014-sikdd-test-of-time-award-winners. Accessed 16 May 2015
- University of Eastern Finland (2015) Clustering datasets. https://cs.joensuu.fi/sipu/datasets/. Accessed 19 Nov 2015
- Zhou ZH, Dai HB (2006) Query-sensitive similarity measure for content-based image retrieval. In: Proceedings of the sixth international conference on data mining, ICDM ’06, IEEE Computer Society, Washington, DC, pp 1211–1215Google Scholar