A Semantics-Based Clustering Approach for Online Laboratories Using K-Means and HAC Algorithms

Abstract

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.

POS‐HC: A Part‐of‐Speech Hierarchical Clustering Approach for Normative Texts Partition

“…This investigation entailed a comparative assessment of the clustering performance of these two methods concerning concise, real-time descriptions from online laboratory repositories. The results of the study indicated that, particularly on small datasets, HAC surpassed the K-Means algorithm in terms of clustering efficacy [13] . In addition, a study explored the latent semantic structure of the label set according to spectral clustering and presented a new evaluation function based on information theory [14].…”

Content Analysis Using Specific Natural Language Processing Methods for Big Data

“…The study of the similarity of some entities is increasingly used in research from all fields: medical, public health, education, management, environment, business, crisis events/situations in society, communication. Through the computational determination of the similarity of some texts and some news transmitted online regarding a special event or situation, it is possible to evaluate the effectiveness of communication actions in influencing society's behavior regarding public health [72], to achieve the semantic grouping of documents [73], to analyze the similarity of the weighting methods of the criteria taken into account in Multicriteria Decision Support Systems in management [74], and to study cerebral activity on several channels in medicine [75].…”