A Semantics-Based Clustering Approach for Online Laboratories Using K-Means and HAC Algorithms
Due to the availability of a vast amount of unstructured data in various forms (e.g., the web, social networks, etc.), the clustering of text documents has become increasingly important. Traditional clustering algorithms have not been able to solve this problem because the semantic relationships bet...
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-01-01
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| Series: | Mathematics |
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| Online Access: | https://www.mdpi.com/2227-7390/11/3/548 |
| _version_ | 1797623931876671488 |
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| author | Saad Hikmat Haji Karwan Jacksi Razwan Mohmed Salah |
| author_facet | Saad Hikmat Haji Karwan Jacksi Razwan Mohmed Salah |
| author_sort | Saad Hikmat Haji |
| collection | DOAJ |
| description | Due to the availability of a vast amount of unstructured data in various forms (e.g., the web, social networks, etc.), the clustering of text documents has become increasingly important. Traditional clustering algorithms have not been able to solve this problem because the semantic relationships between words could not accurately represent the meaning of the documents. Thus, semantic document clustering has been extensively utilized to enhance the quality of text clustering. This method is called unsupervised learning and it involves grouping documents based on their meaning, not on common keywords. This paper introduces a new method that groups documents from online laboratory repositories based on the semantic similarity approach. In this work, the dataset is collected first by crawling the short real-time descriptions of the online laboratories’ repositories from the Web. A vector space is created using frequency-inverse document frequency (TF-IDF) and clustering is done using the K-Means and Hierarchical Agglomerative Clustering (HAC) algorithms with different linkages. Three scenarios are considered: without preprocessing (WoPP); preprocessing with steaming (PPwS); and preprocessing without steaming (PPWoS). Several metrics have been used for evaluating experiments: Silhouette average, purity, V-measure, F1-measure, accuracy score, homogeneity score, completeness and NMI score (consisting of five datasets: online labs, 20 NewsGroups, Txt_sentoken, NLTK_Brown and NLTK_Reuters). Finally, by creating an interactive webpage, the results of the proposed work are contrasted and visualized. |
| first_indexed | 2024-03-11T09:35:46Z |
| format | Article |
| id | doaj.art-a75a26b727da451eb59618d2e3ff7f5e |
| institution | Directory Open Access Journal |
| issn | 2227-7390 |
| language | English |
| last_indexed | 2024-03-11T09:35:46Z |
| publishDate | 2023-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Mathematics |
| spelling | doaj.art-a75a26b727da451eb59618d2e3ff7f5e2023-11-16T17:21:10ZengMDPI AGMathematics2227-73902023-01-0111354810.3390/math11030548A Semantics-Based Clustering Approach for Online Laboratories Using K-Means and HAC AlgorithmsSaad Hikmat Haji0Karwan Jacksi1Razwan Mohmed Salah2Department of Information Technology, Technical College of Informatics-Akre, Duhok Polytechnic University, Akre 42003, IraqDepartment of Computer Science, University of Zakho, Zakho 42002, IraqDepartment of Computer Science, University of Duhok, Duhok 42001, IraqDue to the availability of a vast amount of unstructured data in various forms (e.g., the web, social networks, etc.), the clustering of text documents has become increasingly important. Traditional clustering algorithms have not been able to solve this problem because the semantic relationships between words could not accurately represent the meaning of the documents. Thus, semantic document clustering has been extensively utilized to enhance the quality of text clustering. This method is called unsupervised learning and it involves grouping documents based on their meaning, not on common keywords. This paper introduces a new method that groups documents from online laboratory repositories based on the semantic similarity approach. In this work, the dataset is collected first by crawling the short real-time descriptions of the online laboratories’ repositories from the Web. A vector space is created using frequency-inverse document frequency (TF-IDF) and clustering is done using the K-Means and Hierarchical Agglomerative Clustering (HAC) algorithms with different linkages. Three scenarios are considered: without preprocessing (WoPP); preprocessing with steaming (PPwS); and preprocessing without steaming (PPWoS). Several metrics have been used for evaluating experiments: Silhouette average, purity, V-measure, F1-measure, accuracy score, homogeneity score, completeness and NMI score (consisting of five datasets: online labs, 20 NewsGroups, Txt_sentoken, NLTK_Brown and NLTK_Reuters). Finally, by creating an interactive webpage, the results of the proposed work are contrasted and visualized.https://www.mdpi.com/2227-7390/11/3/548document clusteringsemantic similarityonline laboratoriescrawlingTF-IDFK-means |
| spellingShingle | Saad Hikmat Haji Karwan Jacksi Razwan Mohmed Salah A Semantics-Based Clustering Approach for Online Laboratories Using K-Means and HAC Algorithms Mathematics document clustering semantic similarity online laboratories crawling TF-IDF K-means |
| title | A Semantics-Based Clustering Approach for Online Laboratories Using K-Means and HAC Algorithms |
| title_full | A Semantics-Based Clustering Approach for Online Laboratories Using K-Means and HAC Algorithms |
| title_fullStr | A Semantics-Based Clustering Approach for Online Laboratories Using K-Means and HAC Algorithms |
| title_full_unstemmed | A Semantics-Based Clustering Approach for Online Laboratories Using K-Means and HAC Algorithms |
| title_short | A Semantics-Based Clustering Approach for Online Laboratories Using K-Means and HAC Algorithms |
| title_sort | semantics based clustering approach for online laboratories using k means and hac algorithms |
| topic | document clustering semantic similarity online laboratories crawling TF-IDF K-means |
| url | https://www.mdpi.com/2227-7390/11/3/548 |
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