Exploring other clustering methods and the role of Shannon Entropy in an unsupervised setting

Erin Chelsea  Hathorn; Ahmed Abu Halimeh

doi:10.59400/cai.v2i2.1447

Exploring other clustering methods and the role of Shannon Entropy in an unsupervised setting

Erin Chelsea Hathorn University of Arkansas Little Rock, Little Rock, Arkansas 72204, USA
Ahmed Abu Halimeh University of Arkansas Little Rock, Little Rock, Arkansas 72204, USA

Article ID: 1447

1362 Views, 771 PDF Downloads

DOI: https://doi.org/10.59400/cai.v2i2.1447

Keywords: data curation; data washing machine; data quality; Shannon Entropy; unsupervised clustering; entity resolution; spectral clustering; autoencoders; DBSCAN

Abstract

In the ever-expanding landscape of digital technologies, the exponential growth of data in information science and health informatics presents both challenges and opportunities, demanding innovative approaches to data curation. This study focuses on evaluating various feasible clustering methods within the Data Washing Machine (DWM), a novel tool designed to streamline unsupervised data curation processes. The DWM integrates Shannon Entropy into its clustering process, allowing for adaptive refinement of clustering strategies based on entropy levels observed within data clusters. Rigorous testing of the DWM prototype on various annotated test samples revealed promising outcomes, particularly in scenarios with high-quality data. However, challenges arose when dealing with poor data quality, emphasizing the importance of data quality assessment and improvement for successful data curation. To enhance the DWM’s clustering capabilities, this study explored alternative unsupervised clustering methods, including spectral clustering, autoencoders, and density-based clustering like DBSCAN. The integration of these alternative methods aimed to augment the DWM’s ability to handle diverse data scenarios effectively. The findings demonstrated the practicability of constructing an unsupervised entity resolution engine with the DWM, highlighting the critical role of Shannon Entropy in enhancing unsupervised clustering methods for effective data curation. This study underscores the necessity of innovative clustering strategies and robust data quality assessments in navigating the complexities of modern data landscapes. This content is structured by the following sections: Introduction, Methodology, Results, Discussion, and Conclusion.

References

[1]Al-Ruithe M, Benkhelifa E, Hameed K. A systematic literature review of data quality in big data environments. Journal of Computer and System Sciences. 2020; 107: 50–67. doi: 10.1016/j.jcss.2019.09.004.

[2]Anderson KE, Talburt JR, Hagan NKA, et al. Optimal Starting Parameters for Unsupervised Data Clustering and Cleaning in the Data Washing Machine. Springer Nature Switzerland. 2023; 1–20

[3]Talburt JR, K. A, Pullen D, Claassens L, Wang R. An Iterative, Self-Assessing Entity Resolution System: First Steps toward a Data Washing Machine. International Journal of Advanced Computer Science and Applications. 2020; 11(12). doi: 10.14569/ijacsa.2020.0111279

[4]CE Shannon. A Mathematical Theory of Communication. Bell System Technical Journal. 1948; 27(3): 379–423. doi: 10.1002/j.1538-7305.1948.tb01338.x

[5]Cover TM, Thomas JA. Elements of Information Theory. Wiley-Interscience. 2006.

[6]Yue T, Wang L, Liu L, Joseph KS. Fuzzy Clustering with Entropy Regularization for Interval-Valued Data with an Application to Scientific Journal Citations. Information Sciences. 2021; 553: 68–89.

[7]Batini C, Scannapieco M. Data and Information Quality: Concepts, Methodologies, and Techniques. Springer International Publishing. 2020. doi:10.1007/978-3-030-36202-5.

[8]Johnson L. Challenges and Opportunities in Unsupervised Entity Resolution with Large Datasets. Big Data Research. 2020; 22: 45–59.

[9]Hinton GE, Salakhutdinov RR. Reducing the Dimensionality of Data with Neural Networks. Science. 2006; 313(5786): 504–507. doi: 10.1126/science.1127647

[10]Ester M, Kriegel H-P, Sander J, Xu X. A Density-Based Algorithm for Discovering Clusters in Large Spatial Databases with Noise. In: Proceedings of the Second International Conference on Knowledge Discovery and Data Mining (KDD-96); 1996.

[11]Von Luxburg U. A tutorial on spectral clustering. Statistics and Computing. 2007; 17(4): 395–416. doi: 10.1007/s11222-007-9033-z

[12]Xie J, Girshick R, Farhadi A. Unsupervised deep embedding for clustering analysis. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR); 2021. pp. 5215–5224.

[13]Smith J, Doe A. Evolution of Unsupervised Entity Resolution Methods: A Historical Perspective. Journal of Data Management. 2019; 30(4): 15–29.

[14]Lim YY, Chan YK, Ang TPP. Shannon Entropy Used for Feature Extractions of Optical Patterns in the Context of Structural Health Monitoring. Journal of Structural Integrity. 2021; 15(2): 123–135.

[15]Hu J, Pei J, Tao Y. Clustering Heterogeneous Categorical Data Using Enhanced Mini-Batch K-Means with Entropy Distance Measure. Data Mining and Knowledge Discovery. 2021; 35: 317–349.

[16]Kiselev VY, Andrews TS, Hemberg M. Challenges in unsupervised clustering of single-cel RNA-seq data. Nature Reviews Genetics. 2020; 21(5): 273–282. doi:10.1038/s41576-020-00258-6

[17]Zhang C, Yang C, Zhao Y. Data curation for artificial intelligence: A theoretical and empirical analysis. Journal of the Association for Information Science and Technology. 2021; 72(4): 403–418. doi:10.1002/asi.24414.

[18]Zhang Y, Lu J, Wang X. Analyzing urban traffic patterns based on Shannon entropy. Entropy. 2020; 22(10): 1081. doi:10.3390/e22101081.

[19]Park YR, Lee SI, Seo HJ. Data curation in big data environments: Challenges and strategies. Journal of Big Data. 2022; 9(1): 12. doi:10.1186/s40537-022-00547-7.

[20]Chen Q, Wang Z, Li L. Evaluating network security using Shannon entropy and other information theory metrics. Entropy. 2020; 22(9): 1032. doi:10.3390/e22091032

Published

2024-08-09

How to Cite

Hathorn, E. C., & Abu Halimeh, A. (2024). Exploring other clustering methods and the role of Shannon Entropy in an unsupervised setting. Computing and Artificial Intelligence, 2(2), 1447. https://doi.org/10.59400/cai.v2i2.1447

Download Citation

Issue

Vol. 2 No. 2 (2024)

Section

Article

This work is licensed under a Creative Commons Attribution 4.0 International License.

Editor-in-Chief

Prof. Shaohua Wan
University of Electronic Science and Technology of China, China

eISSN

3029-2786

Publication Frequency

Semi-annual

About the Publisher

Academic Publishing insists on taking academic exchange and publication as the main line, carrying out comprehensive management based on science and technology, and fully exploring excellent international publishing resources. Within 5 years, it will form a strategic framework and scale with science (S), technology (T), medicine (M), education (E), and humanities and arts (H) as the main publishing fields. Academic Publishing is headquartered in Singapore and based in Malaysia, with the United States and China providing the main scientific and academic resources. At the same time, it has established long-term good cooperative relations with other publishing companies, scientific research communities, and academic organizations in more than a dozen countries and regions. Academic Publishing uses English and Chinese as its main publishing languages, mainly publishing books, journals, and conference papers in print and online. The vast majority of publications follow the international open access policy, providing stable and long-term quality and professional publications. With the joint efforts of the expert team and our professional editorial team, our publications will gradually be indexed by international databases in stages to provide convenient and professional retrieval for various scholars. At the same time, manuscripts we accept will be subject to the peer review principle, and cutting-edge and innovative research articles will be preferentially accepted for peer reference and discussion. All kinds of our publications are welcome for peer to contribute, access, and download.

more

Volume Arrangement

2025

2024

2023

Featured Articles

Identifying voices using convolution neural network models AlexNet and ResNet

Deep learning (DL) techniques which implement deep neural networks became popular due to the increase of high-performance computing facilities. DL achieves higher power and flexibility due to its ability to process many features when it deals with unstructured data. DL algorithm passes the data through several layers; each layer is capable of extracting features progressively and passes it to the next layer. Initial layers extract low-level features, and succeeding layers combine features to form a complete representation. This research attempts to utilize DL techniques for identifying sounds. The development in DL models has extensively covered classification and verification of objects through images. However, there have not been any notable findings concerning identification and verification of the voice of an individual from different other individuals using DL techniques. Hence, the proposed research aims to develop DL techniques capable of isolating the voice of an individual from a group of other sounds and classify them based on the use of convolutional neural networks models AlexNet and ResNet, that are used in voice identification. We achieved the classification accuracy of ResNet and AlexNet model for the problem of voice identification is 97.2039 % and 65.95% respectively, in which ResNet model achieves the best result.

Revolutionizing Neurosurgery and Neurology: The transformative impact of artificial intelligence in healthcare

The integration of artificial intelligence (AI) has brought about a paradigm shift in the landscape of Neurosurgery and Neurology, revolutionizing various facets of healthcare. This article meticulously explores seven pivotal dimensions where AI has made a substantial impact, reshaping the contours of patient care, diagnostics, and treatment modalities. AI’s exceptional precision in deciphering intricate medical imaging data expedites accurate diagnoses of neurological conditions. Harnessing patient-specific data and genetic information, AI facilitates the formulation of highly personalized treatment plans, promising more efficacious therapeutic interventions. The deployment of AI-powered robotic systems in neurosurgical procedures not only ensures surgical precision but also introduces remote capabilities, mitigating the potential for human error. Machine learning models, a core component of AI, play a crucial role in predicting disease progression, optimizing resource allocation, and elevating the overall quality of patient care. Wearable devices integrated with AI provide continuous monitoring of neurological parameters, empowering early intervention strategies for chronic conditions. AI’s prowess extends to drug discovery by scrutinizing extensive datasets, offering the prospect of groundbreaking therapies for neurological disorders. The realm of patient engagement witnesses a transformative impact through AI-driven chatbots and virtual assistants, fostering increased adherence to treatment plans. Looking ahead, the horizon of AI in Neurosurgery and Neurology holds promises of heightened personalization, augmented decision-making, early intervention, and the emergence of innovative treatment modalities. This narrative is one of optimism and collaboration, depicting a synergistic partnership between AI and healthcare professionals to propel the field forward and significantly enhance the lives of individuals grappling with neurological challenges. This article provides an encompassing view of AI’s transformative influence in Neurosurgery and Neurology, highlighting its potential to redefine the landscape of patient care and outcomes.

Enhancing user experience in large language models through human-centered design: Integrating theoretical insights with an experimental study to meet diverse software learning needs with a single document knowledge base

This paper begins with a theoretical exploration of the rise of large language models (LLMs) in Human-Computer Interaction (HCI), their impact on user experience (HX) and related challenges. It then discusses the benefits of Human-Centered Design (HCD) principles and the possibility of their application within LLMs, subsequently deriving six specific HCD guidelines for LLMs. Following this, a preliminary experiment is presented as an example to demonstrate how HCD principles can be employed to enhance user experience within GPT by using a single document input to GPT’s Knowledge base as new knowledge resource to control the interactions between GPT and users, aiming to meet the diverse needs of hypothetical software learners as much as possible. The experimental results demonstrate the effect of different elements’ forms and organizational methods in the document, as well as GPT’s relevant configurations, on the interaction effectiveness between GPT and software learners. A series of trials are conducted to explore better methods to realize text and image displaying, and jump action. Two template documents are compared in the aspects of the performances of the four interaction modes. Through continuous optimization, an improved version of the document was obtained to serve as a template for future use and research.

Clustering data analytics of urban land use for change detection

In this study, the author proposes and details a workflow for the spatial-temporal demarcation of urban areal features in 8 cities of Tamilnadu, India. During the inception phase, functional requirements and non-functional parameters are analyzed and designed, within a suitable pixel area and object-oriented derived paradigm. Land use categories are defined from OpenStreetMap (OSM) related works with the scope of conducting climate change, using multispectral sensors onboard Landsat series. Furthermore, we augment the bands dataset with Spatially Invariant Feature Transform (SIFT), Normalized Difference Vegetation Index (NDVI), Normalized Difference Built-Up Index (NDBI), Leaf Area Index (LAI), and Texture base indices, as a means of spatially integrating auto-covariance to stationarity patterns. In doing so, change detection can be pursuit by scaling up the segmentation of regional/zonal boundaries in a multi-dimensional environment, with the aid of Wide Area Networks (WAN) cluster computers such as the BEOWULF/Google Earth Engine clusters. GeoAnalytical measures are analyzed in the design of local and zonal spatial models (GRID, RASTER, DEM, IMAGE COLLECTION). Finally, multi variate geostatistical works are ensued for precision and recall in predictive data analytics. The author proposes reusing machine learning tools (filtering by attribute-based indexing in PaaS clouds) for pattern recognition and visualization of features and feature collection.

Application of computer vision in livestock and crop production—A review

Nowadays, it is a challenge for farmers to produce healthier food for the world population and save land resources. Recently, the integration of computer vision technology in field and crop production ushered in a new era of innovation and efficiency. Computer vision, a subfield of artificial intelligence, leverages image and video analysis to extract meaningful information from visual data. In agriculture, this technology is being utilized for tasks ranging from disease detection and yield prediction to animal health monitoring and quality control. By employing various imaging techniques, such as drones, satellites, and specialized cameras, computer vision systems are able to assess the health and growth of crops and livestock with unprecedented accuracy. The review is divided into two parts: Livestock and Crop Production giving the overview of the application of computer vision applications within agriculture, highlighting its role in optimizing farming practices and enhancing agricultural productivity.