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A state-of-the-art survey on semantic similarity for document clustering using GloVe and density-based algorithms

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Τίτλος: A state-of-the-art survey on semantic similarity for document clustering using GloVe and density-based algorithms
Συγγραφείς: Mohammed, Shapol M., Jacksi, Karwan, Zeebaree, Subhi R. M.
Στοιχεία εκδότη: Zenodo
Έτος έκδοσης: 2021
Συλλογή: Zenodo
Θεματικοί όροι: DBSCAN, Density-based algorithm, DPC, GloVe word embedding, Similarity measurement, Evaluation measurement
Περιγραφή: Semantic similarity is the process of identifying relevant data semantically. The traditional way of identifying document similarity is by using synonymous keywords and syntactician. In comparison, semantic similarity is to find similar data using meaning of words and semantics. Clustering is a concept of grouping objects that have the same features and properties as a cluster and separate from those objects that have different features and properties. In semantic document clustering, documents are clustered using semantic similarity techniques with similarity measurements. One of the common techniques to cluster documents is the density-based clustering algorithms using the density of data points as a main strategic to measure the similarity between them. In this paper, a state-of-the-art survey is presented to analyze the density-based algorithms for clustering documents. Furthermore, the similarity and evaluation measures are investigated with the selected algorithms to grasp the common ones. The delivered review revealed that the most used density-based algorithms in document clustering are DBSCAN and DPC. The most effective similarity measurement has been used with densitybased algorithms, specifically DBSCAN and DPC, is Cosine similarity with F-measure for performance and accuracy evaluation.
Τύπος εγγράφου: article in journal/newspaper
Γλώσσα: English
Relation: https://zenodo.org/records/7084205; oai:zenodo.org:7084205
DOI: 10.11591/ijeecs.v22.i1.pp552-562
Διαθεσιμότητα: https://doi.org/10.11591/ijeecs.v22.i1.pp552-562
https://zenodo.org/records/7084205
Rights: Creative Commons Attribution 4.0 International ; cc-by-4.0 ; https://creativecommons.org/licenses/by/4.0/legalcode
Αριθμός Καταχώρησης: edsbas.79EFC2EC
Βάση Δεδομένων: BASE
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  – Url: https://doi.org/10.11591/ijeecs.v22.i1.pp552-562#
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  Data: A state-of-the-art survey on semantic similarity for document clustering using GloVe and density-based algorithms
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  Data: <searchLink fieldCode="AR" term="%22Mohammed%2C+Shapol+M%2E%22">Mohammed, Shapol M.</searchLink><br /><searchLink fieldCode="AR" term="%22Jacksi%2C+Karwan%22">Jacksi, Karwan</searchLink><br /><searchLink fieldCode="AR" term="%22Zeebaree%2C+Subhi+R%2E+M%2E%22">Zeebaree, Subhi R. M.</searchLink>
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  Data: Semantic similarity is the process of identifying relevant data semantically. The traditional way of identifying document similarity is by using synonymous keywords and syntactician. In comparison, semantic similarity is to find similar data using meaning of words and semantics. Clustering is a concept of grouping objects that have the same features and properties as a cluster and separate from those objects that have different features and properties. In semantic document clustering, documents are clustered using semantic similarity techniques with similarity measurements. One of the common techniques to cluster documents is the density-based clustering algorithms using the density of data points as a main strategic to measure the similarity between them. In this paper, a state-of-the-art survey is presented to analyze the density-based algorithms for clustering documents. Furthermore, the similarity and evaluation measures are investigated with the selected algorithms to grasp the common ones. The delivered review revealed that the most used density-based algorithms in document clustering are DBSCAN and DPC. The most effective similarity measurement has been used with densitybased algorithms, specifically DBSCAN and DPC, is Cosine similarity with F-measure for performance and accuracy evaluation.
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