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Soft Computing

, Volume 22, Issue 8, pp 2463–2469 | Cite as

Multiple attribute similarity hypermatching

  • Ronald Yager
  • Fred Petry
  • Paul Elmore
Foundations
  • 176 Downloads

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.

Keywords

Attribute importance Combination of attributes Amplification Soft matching Similarity 

Notes

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.

Compliance with ethical standards

Conflict of interest

Authors Yager, Elmore and Petry declare they have no conflict of interest.

Ethical approval

This article does not contain any studies with human participants or animals performed by any of the authors.

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Copyright information

© Springer-Verlag GmbH Germany (outside the usa) 2017

Authors and Affiliations

  1. 1.Machine Intelligence InstituteIona CollegeNew RochelleUSA
  2. 2.Geospatial Science and Technology Branch, Bldg. 1005 Naval Research LaboratoryStennis Space CenterHancock CountyUSA

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