A new text-based w-distance metric to find the perfect match between words

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.

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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.

Item Type:Article
Impact Factor:cited By 0
Uncontrolled 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
ID Code:23450
Deposited By: Ms Sharifah Fahimah Saiyed Yeop
Deposited On:19 Aug 2021 07:19
Last Modified:19 Aug 2021 07:19

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