@inproceedings{8f0ededf0c3c4c7289beebd1041fb711,
title = "ABC-SG: A new artificial bee colony algorithm-based distance of sequential data using sigma grams",
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.",
keywords = "Artificial bee colony, Distance metric, Extended edit distance, N-grams, Sequential data",
author = "{Muhammad Fuad}, {Muhammad Marwan}",
year = "2012",
month = jan,
day = "1",
language = "English",
series = "Conferences in Research and Practice in Information Technology Series",
publisher = "Australian Computer Society",
pages = "85--91",
editor = "Yanchang Zhao and Peter Christen and Jiuyong Li and Kennedy, {Paul J.}",
booktitle = "Proceedings of the 10th Australasian Data Mining Conference, AusDM 2012",
address = "Australia",
}