eprintid: 13942
rev_number: 2
eprint_status: archive
userid: 1
dir: disk0/00/01/39/42
datestamp: 2023-11-10 03:28:30
lastmod: 2023-11-10 03:28:30
status_changed: 2023-11-10 01:52:21
type: article
metadata_visibility: show
creators_name: Ali, M.
creators_name: Jung, L.T.
creators_name: Hosam, O.
creators_name: Wagan, A.A.
creators_name: Shah, R.A.
creators_name: Khayyat, M.
title: A new text-based w-distance metric to find the perfect match between words
ispublished: pub
keywords: Data mining; Genetic algorithms; Nearest neighbor search; Pattern recognition; Personnel, Cosine similarity; Data mining applications; distance/similarity metric; Euclidean distance; Instance based learning; k-NN algorithm; Similarity distance; text match, Text mining
note: cited By 1
abstract: The k-NN algorithm is an instance-based learning algorithm which is widely used in the data mining applications. The core engine of the k-NN algorithm is the distance/similarity function. The performance of the k-NN algorithm varies with the selection of distance function. The traditional distance/similarity functions in k-NN do not perfectly handle the mix-mode words such as when one string has multiple substrings/words. For example, a two-word string of 'Employee Name', a one-word string of 'Name' or more than one word such as, 'Name of Employee'. This ambiguity is faced by different distance/similarity functions causing difficulties in finding the perfect match of words. To improve the perfect-match calculation functionality in the traditional k-NN algorithm, a new similarity distance metric is developed and named as word-distance (w-distance). The perfect match will help us to identify the exact required value. The proposed w-distance is a hybrid of distance and similarity in nature because it is to handle dissimilarity and similarity features of strings at the same time. The simulation results showed that w-distance has a better impact on the performance of the k-NN algorithm as compared to the Euclidean distance and the cosine similarity. © 2020-IOS Press and the authors. All rights reserved.
date: 2020
publisher: IOS Press
official_url: https://www.scopus.com/inward/record.uri?eid=2-s2.0-85081537860&doi=10.3233%2fJIFS-179552&partnerID=40&md5=2f8ba723a873404c83134a9f0365fe1b
id_number: 10.3233/JIFS-179552
full_text_status: none
publication: Journal of Intelligent and Fuzzy Systems
volume: 38
number: 3
pagerange: 2661-2672
refereed: TRUE
issn: 10641246
citation:   Ali, M. and Jung, L.T. and Hosam, O. and Wagan, A.A. and Shah, R.A. and Khayyat, M.  (2020) A new text-based w-distance metric to find the perfect match between words.  Journal of Intelligent and Fuzzy Systems, 38 (3).  pp. 2661-2672.  ISSN 10641246