Accession Number:

ADA622081

Title:

Non-Metric Similarity Measures

Descriptive Note:

Final rept. 28 Mar 2013-27 Mar 2015

Corporate Author:

FEDERATION UNIV CHURCHILL (AUSTRALIA)

Personal Author(s):

Report Date:

2015-03-26

Pagination or Media Count:

28.0

Abstract:

This was an extension of mass theory to non-metric similarity measures. The non-metric similarity measures were created as a generalization of mass estimation from a unary function to a binary function. Unlike the traditional similarity measure that is based on geometric difference between two instances, the mass-based measure takes data distribution into account. It is demonstrated that the new measure results in better performance in applying to information retrieval task. A derivative of mass measure called relative mass was also investigated using three implementations. The research in relative mass was expanded to two tasks In anomaly detection relative mass is used to overcome one weakness of current mass-based anomaly detectors using a tree-based approach and a nearest-neighbor-based approach in clustering, relative mass is used to recondition density-based clustering algorithms to successfully find clusters with varying densities.

Subject Categories:

  • Information Science
  • Theoretical Mathematics
  • Operations Research

Distribution Statement:

APPROVED FOR PUBLIC RELEASE