A state-of-the-art survey on semantic similarity for document clustering using GloVe and density-based algorithms
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 conc...
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| Main Authors: | , , |
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| Format: | EJournal Article |
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Institute of Advanced Engineering and Science,
2021-04-01.
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| Online Access: | Get fulltext |
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| LEADER | 02689 am a22003133u 4500 | ||
|---|---|---|---|
| 001 | ijeecs23698_14847 | ||
| 042 | |a dc | ||
| 100 | 1 | 0 | |a M. Mohammed, Shapol |e author |
| 100 | 1 | 0 | |e contributor |
| 700 | 1 | 0 | |a Jacksi, Karwan |e author |
| 700 | 1 | 0 | |a R. M. Zeebaree, Subhi |e author |
| 245 | 0 | 0 | |a A state-of-the-art survey on semantic similarity for document clustering using GloVe and density-based algorithms |
| 260 | |b Institute of Advanced Engineering and Science, |c 2021-04-01. | ||
| 500 | |a https://ijeecs.iaescore.com/index.php/IJEECS/article/view/23698 | ||
| 520 | |a 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 density-based algorithms, specifically DBSCAN and DPC, is Cosine similarity with F-measure for performance and accuracy evaluation. | ||
| 540 | |a Copyright (c) 2021 Institute of Advanced Engineering and Science | ||
| 540 | |a http://creativecommons.org/licenses/by-nc/4.0 | ||
| 546 | |a eng | ||
| 690 | |||
| 690 | |a Similarity Measurement; Evaluation Measurement; DBSCAN; Density-based algorithm; DPC GloVe word embedding | ||
| 655 | 7 | |a info:eu-repo/semantics/article |2 local | |
| 655 | 7 | |a info:eu-repo/semantics/publishedVersion |2 local | |
| 655 | 7 | |2 local | |
| 786 | 0 | |n Indonesian Journal of Electrical Engineering and Computer Science; Vol 22, No 1: April 2021; 552-562 | |
| 786 | 0 | |n 2502-4760 | |
| 786 | 0 | |n 2502-4752 | |
| 786 | 0 | |n 10.11591/ijeecs.v22.i1 | |
| 787 | 0 | |n https://ijeecs.iaescore.com/index.php/IJEECS/article/view/23698/14847 | |
| 856 | 4 | 1 | |u https://ijeecs.iaescore.com/index.php/IJEECS/article/view/23698/14847 |z Get fulltext |