%I IOS Press
%P 2661-2672
%O cited By 1
%K 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
%J Journal of Intelligent and Fuzzy Systems
%A M. Ali
%A L.T. Jung
%A O. Hosam
%A A.A. Wagan
%A R.A. Shah
%A M. Khayyat
%T A new text-based w-distance metric to find the perfect match between words
%R 10.3233/JIFS-179552
%X 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.
%L scholars13942
%V 38
%D 2020
%N 3