ABC-SG: A new artificial bee colony algorithm-based distance of sequential data using sigma grams

Research output: Chapter in Book/Report/Conference proceedingConference proceeding

Abstract

The problem of similarity search is one of the main problems in computer science. This problem has many applications in text-retrieval, web search, computational biology, bioinformatics and others. Similarity between two data objects can be depicted using a similarity measure or a distance metric. There are numerous distance metrics in the literature, some are used for a particular data type, and others are more general. In this paper we present a new distance metric for sequential data which is based on the sum of n-grams. The novelty of our distance is that these n-grams are weighted using artificial bee colony; a recent optimization algorithm based on the collective intelligence of a swarm of bees on their search for nectar. This algorithm has been used in optimizing a large number of numerical problems. We validate the new distance experimentally.

Original languageEnglish
Title of host publicationProceedings of the 10th Australasian Data Mining Conference, AusDM 2012
EditorsYanchang Zhao, Peter Christen, Jiuyong Li, Paul J. Kennedy
PublisherAustralian Computer Society
Pages85-91
Number of pages7
ISBN (Electronic)9781921770142
Publication statusPublished - 1 Jan 2012
Externally publishedYes

Publication series

NameConferences in Research and Practice in Information Technology Series
Volume134
ISSN (Print)1445-1336

Keywords

  • Artificial bee colony
  • Distance metric
  • Extended edit distance
  • N-grams
  • Sequential data

ASJC Scopus subject areas

  • Computer Networks and Communications
  • Computer Science Applications
  • Hardware and Architecture
  • Information Systems
  • Software

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