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7. Enhancing Cluster Quality of Numerical Datasets with Domain Ontology

Author

Heiyanthuduwage, Sudath Rohitha, Rahman, Md Anisur, and Islam, Md Zahidul

Subjects
Computer Science - Databases, Computer Science - Artificial Intelligence, Computer Science - Machine Learning
Abstract

Ontology-based clustering has gained attention in recent years due to the potential benefits of ontology. Current ontology-based clustering approaches have mainly been applied to reduce the dimensionality of attributes in text document clustering. Reduction in dimensionality of attributes using ontology helps to produce high quality clusters for a dataset. However, ontology-based approaches in clustering numerical datasets have not been gained enough attention. Moreover, some literature mentions that ontology-based clustering can produce either high quality or low-quality clusters from a dataset. Therefore, in this paper we present a clustering approach that is based on domain ontology to reduce the dimensionality of attributes in a numerical dataset using domain ontology and to produce high quality clusters. For every dataset, we produce three datasets using domain ontology. We then cluster these datasets using a genetic algorithm-based clustering technique called GenClust++. The clusters of each dataset are evaluated in terms of Sum of Squared-Error (SSE). We use six numerical datasets to evaluate the performance of our ontology-based approach. The experimental results of our approach indicate that cluster quality gradually improves from lower to the higher levels of a domain ontology., Comment: 6 Pages, IEEE CSDE2022 Conference Paper

Published
2023

23. S-Image (Situation Image): A New Technique for Data Aggregation in Cloud Server for IoT Based Smart City

Author

Hossain, SK Alamgir, Rahman, Md. Anisur, Hossain, M. Anwar, Kacprzyk, Janusz, Series Editor, Gomide, Fernando, Advisory Editor, Kaynak, Okyay, Advisory Editor, Liu, Derong, Advisory Editor, Pedrycz, Witold, Advisory Editor, Polycarpou, Marios M., Advisory Editor, Rudas, Imre J., Advisory Editor, Wang, Jun, Advisory Editor, and Arai, Kohei, editor

Published
2023
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25. A Novel Incremental Clustering Technique with Concept Drift Detection

Author

Woodbright, Mitchell D., Rahman, Md Anisur, and Islam, Md Zahidul

Subjects
Computer Science - Machine Learning, Statistics - Machine Learning
Abstract

Data are being collected from various aspects of life. These data can often arrive in chunks/batches. Traditional static clustering algorithms are not suitable for dynamic datasets, i.e., when data arrive in streams of chunks/batches. If we apply a conventional clustering technique over the combined dataset, then every time a new batch of data comes, the process can be slow and wasteful. Moreover, it can be challenging to store the combined dataset in memory due to its ever-increasing size. As a result, various incremental clustering techniques have been proposed. These techniques need to efficiently update the current clustering result whenever a new batch arrives, to adapt the current clustering result/solution with the latest data. These techniques also need the ability to detect concept drifts when the clustering pattern of a new batch is significantly different from older batches. Sometimes, clustering patterns may drift temporarily in a single batch while the next batches do not exhibit the drift. Therefore, incremental clustering techniques need the ability to detect a temporary drift and sustained drift. In this paper, we propose an efficient incremental clustering algorithm called UIClust. It is designed to cluster streams of data chunks, even when there are temporary or sustained concept drifts. We evaluate the performance of UIClust by comparing it with a recently published, high-quality incremental clustering algorithm. We use real and synthetic datasets. We compare the results by using well-known clustering evaluation criteria: entropy, sum of squared errors (SSE), and execution time. Our results show that UIClust outperforms the existing technique in all our experiments., Comment: 9 pages, 7 figures

Published
2020

45. The Need for Understanding Disaster Risk for Resilient City Development

Author

Tanjeela, Mumita, Rahman, Md. Anisur, Dahiya, Bharat, Series Editor, Kirby, Andrew, Editorial Board Member, Friedberg, Erhard, Editorial Board Member, Singh, Rana P. B., Editorial Board Member, Yu, Kongjian, Editorial Board Member, El Sioufi, Mohamed, Editorial Board Member, Campbell, Tim, Editorial Board Member, Hayashi, Yoshitsugu, Editorial Board Member, Bai, Xuemei, Editorial Board Member, Haase, Dagmar, Editorial Board Member, Huong, Le Thi Thu, editor, and Pomeroy, George M., editor

Published
2021
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49. Contributors

Author

Bolonio, David, primary, Canoira, Laureano, additional, Chen, Fuying, additional, dos Santos Oliveira, Henrique, additional, Efthymiou, Marina, additional, Fernández-Yáñez, Pablo, additional, García-Contreras, Reyes, additional, García-Martínez, María-Jesús, additional, Gómez, Arántzazu, additional, Gómez-Castro, Fernando Israel, additional, Gutiérrez-Antonio, Claudia, additional, Hernández, Salvador, additional, Hossain, Md. Shahadat, additional, Islam, M. Amirul, additional, Karim, Ahasanul, additional, Moradi, Pantea, additional, Nayeem, Abdullah, additional, Ortega, Marcelo F., additional, Osseweijer, Patricia, additional, Palmeros Parada, Mar, additional, Pasa, Vânya Marcia Duarte, additional, Pashaei Kamali, Farahnaz, additional, Posada, John A., additional, Rahman, Md. Anisur, additional, Rahman, Mohammad Jalilur, additional, Romero-Izquierdo, Araceli Guadalupe, additional, Ryley, Tim, additional, Saidi, Majid, additional, Scaldadaferri, Cristiane Almeida, additional, Soriano, José A., additional, van der Putten, Wim H., additional, van der Wielen, Luuk A.M., additional, van Loosdrecht, Mark, additional, Vela-García, Nicolas, additional, Yang, Qing, additional, and Yousuf, Abu, additional

Published
2022
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