Abstract
An approach to objects or events similarity is based on the similarity of the data values of the specific attributes. Similarity is refined by considering importance weights for attributes and also the issues of unusual attribute values where the concept of importance amplification is used to provide soft matching of objects or events We then introduce extensions to hypermatching where certain combinations of attributes are relevant. This is approached by modeling how to represent commonly occurring attribute data values whose co-occurrence is uncommon. Certainly not all attribute combinations are typically of the same interest. What can be expected is that for a particular context or application, some subset of the attributes is being focused upon. As an application, we illustrate the importance of considering combinations of attribute values in assessing evidence in geospatial profiling.
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This is based on comments from one of the reviewers.
References
Anderson D, Ros M, Keller J, Cuellar M, Popescu M, Delgado M, Vila A (2012) Similarity measure for anomaly detection and comparing human behaviors. Int J Intell Syst 27:733–756
Boyd D, Crawford K (2012) Critical questions for big data. Inf Commun Soc 15(5):662–679
Brown A, Smith A, Elmhurst O (2002) The combined use of pollen and soil analyses in a search and subsequent murder investigation. J Forensic Sci 47:614–618
Buckles B, Petry F (1982) A fuzzy representation for relational data bases. Fuzzy Sets Syst 7(3):213–226
Bustince H (2000) Indicator of inclusion grade for interval-valued fuzzy sets: application to approximate reasoning based on interval-valued fuzzy sets. Int J Approx Reason 23(3):137–209
Bustince H, Mohedano V, Barrenechea E, Pagola M (2006) Definition and construction of fuzzy DI-subsethood measures. Inf Sci 176(21):3190–3231
Bustince H, Barrenechea E, Pagola M (2008) Relationship between restricted dissimilarity functions, restricted equivalence functions and normal EN-functions: Image thresholding invariant. Pattern Recognit Lett 29(4):525–536
Canter D, Youngs D (2008) Principles of geographical offender profiling. Ashgate Publishing, Farnham
Castillo E (1988) Extreme value theory in engineering. Academic Press, San Diego, CA
Chen S (2010) Multimedia databases and data management: a survey. Int J Multimed Data Eng Manag 1(1):4–15
Coles S (2001) An introduction to statistical modeling of extreme values. Springer, London
Deza M, Deza E (2014) Encyclopedia of distances, 3rd edn. Springer, Heidleberg
Elmasri R, Navathe S (2010) Fundamentals of database systems, 6th edn. Addison-Wesley, Boston
Kantardzic M (2011) Data mining: concepts, models, methods and algorithms. IEEE Press, Piscataway
Lindgren G, Rootzen H (1987) Extreme values: theory and technical applications. Scand J Stat 14:241–279
Michael K, Miller KB (2013) Big data: new opportunities and new challenges. IEEE Comput 46(6):22–24
Novak S (2011) Extreme values methods with applications to finance. Chapman and Hall, London
Nwosu K, Thurasiingham B, Berra B (2011) Multi-media database systems: design and implementation. Kluwer, Norwell
Pye K (2007) Geological and soil evidence: forensic applications. CRC Press, Boca Raton
Rossmo K (2000) Geographical profiling. CRC Press, Boca Raton
Santini S, Jain R (1999) Similarity measures. IEEE Trans Pattern Anal Mach Intell 21(9):871–883
Shafer G (1976) A mathematical theory of evidence. Princeton University Press, Princeton
Temkin L (1994) A continuum argument for intransitivity. Philos Public Aff 25(3):175–210
Tung A, Zhang R, Koudas N, Ooi B (2006) Similarity search: a matching based approach. In: Proceedings of very large database conference, pp 631–642
Tversky A (1969) Intransitivity of preferences. Psychol Rev 76(1):31–48
Tversky A, Kranz D (1982) Similarity, separability and the triangle inequality. Psychol Rev 89:123–154
Witten I, Frank E, Hall M (2011) Data mining: practical machine learning tools and techniques, 3rd edn. Morgan Kaufmann, San Francisco
Yager R, Petry F (2014) Hyper matching: similarity matching with extreme values. IEEE Trans Fuzzy Syst 22(4):949–957
Zadeh L (1971) Similarity relations and fuzzy orderings. Inf Sci 3:177–200
Zadeh L (1978) Fuzzy sets as a basis for a theory of possibility. Fuzzy Sets Syst 1:3–28
Zezula P, Amato G, Dohnal V, Batko M (2006) Similarity search: the metric space approach. Springer, New York
Acknowledgements
Elmore and Petry were supported in part by the Naval Research Laboratory’s Base Program, Program Element No. 0602435 N. Ronald Yager has been in part supported by ONR Grant Award Number N00014-13-1-0626.
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Communicated by A. Di Nola.
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Yager, R., Petry, F. & Elmore, P. Multiple attribute similarity hypermatching. Soft Comput 22, 2463–2469 (2018). https://doi.org/10.1007/s00500-017-2721-5
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DOI: https://doi.org/10.1007/s00500-017-2721-5