With the rapid development of social economy, environmental problems have become increasingly prominent. Quality management is an important link in offshore engineering projects. This paper first introduces the research status at home and abroad in recent years, summarizes the construction background and main problems of China's shipbuilding industry and water conservancy projects, and then analyzes in detail the common methods and applications in the field of marine engineering technology and construction engineering, Finally, it is proposed to establish a perfect and effective quality management mechanism for construction projects using clustering method and verify that this mode plays a good role in actual engineering projects through specific cases. The performance test of the management system is carried out. The test results show that the quality management system for offshore engineering projects based on Clustering algorithm has a short data processing time, low delay time and low memory consumption.

Zecevic Petar. Quality Management of Offshore Engineering Project Based on Clustering Algorithm. Frontiers in Ocean Engineering (2022), Vol. 3, Issue 4: 10-17. https://doi.org/10.38007/FOE.2022.030402.

[1] Wafa Zubair Al-Dyani, Farzana Kabir Ahmad, Siti Sakira Kamaruddin:Adaptive Binary Bat and Markov Clustering Algorithms for Optimal Text Feature Selection in News Events Detection Model. IEEE Access 10: 85655-85676 (2022). https://doi.org/10.1109/ACCESS.2022.3198654

[2] Wafa Zubair Al-Dyani, Farzana Kabir Ahmad, Siti Sakira Kamaruddin:Adaptive Binary Bat and Markov Clustering Algorithms for Optimal Text Feature Selection in News Events Detection Model. IEEE Access 10: 85655-85676 (2022). https://doi.org/10.1109/ACCESS.2022.3198654

[3] Ahmed Salih Al-Obaidi, Mohammed Ahmed Jubair, Izzatdin Abdul Aziz, Mohd Riduan Ahmad, Salama A. Mostafa, Hairulnizam Mahdin, Abdullah Talaat Al-Tickriti, Mustafa Hamid Hassan:Cauchy Density-Based Algorithm for VANETs Clustering in 3D Road Environments. IEEE Access 10: 76376-76385 (2022). https://doi.org/10.1109/ACCESS.2022.3187698

[4] Claude Cariou, Steven Le Moan, Kacem Chehdi:A Novel Mean-Shift Algorithm for Data Clustering. IEEE Access 10: 14575-14585 (2022). https://doi.org/10.1109/ACCESS.2022.3147951

[5] Rahma Gantassi, Zaki Masood, Yonghoon Choi:Enhancing QoS and Residual Energy by Using of Grid-Size Clustering, K-Means, and TSP Algorithms With MDC in LEACH Protocol. IEEE Access 10: 58199-58211 (2022). https://doi.org/10.1109/ACCESS.2022.3178434

[6] Julio Guillen-Garcia, Daniel Palacios-Alonso, Enrique Cabello, Cristina Conde:Unsupervised Adaptive Multi-Object Tracking-by-Clustering Algorithm With a Bio-Inspired System. IEEE Access 10: 24895-24908 (2022). https://doi.org/10.1109/ACCESS.2022.3154895

[7] Meenakshi Kaushal, Q. M. Danish Lohani:Intuitionistic Fuzzy c-Ordered Means Clustering Algorithm. IEEE Access 10: 26271-26281 (2022). https://doi.org/10.1109/ACCESS.2022.3155869

[8] Ji-Eun Kim, Chang-Hun Lee, Mun Yong Yi:New Weapon Target Assignment Algorithms for Multiple Targets Using a Rotational Strategy and Clustering Approach. IEEE Access 10: 43738-43750 (2022). https://doi.org/10.1109/ACCESS.2022.3168718

[9] Jeong-Seok Lee, Hyeong-Tak Lee, Ik-Soon Cho:Maritime Traffic Route Detection Framework Based on Statistical Density Analysis From AIS Data Using a Clustering Algorithm. IEEE Access 10: 23355-23366 (2022). https://doi.org/10.1109/ACCESS.2022.3154363

[10] Mostafa Raeisi, Abu B. Sesay:A Distance Metric for Uneven Clusters of Unsupervised K-Means Clustering Algorithm. IEEE Access 10: 86286-86297 (2022). https://doi.org/10.1109/ACCESS.2022.3198992

[11] Yaser Ali Shah, Farhan Aadil, Amaad Khalil, Muhammad Assam, Ibrahim Abunadi, Ala Saleh D. Alluhaidan, Fahd N. Al-Wesabi:An Evolutionary Algorithm-Based Vehicular Clustering Technique for VANETs. IEEE Access 10: 14368-14385 (2022). https://doi.org/10.1109/ACCESS.2022.3145905

[12] Angeliki Koutsimpela, Konstantinos D. Koutroumbas:A new stochastic gradient descent possibilistic clustering algorithm. AI Commun. 35(2): 47-64 (2022). https://doi.org/10.3233/AIC-210125

[13] Cuong Trinh, Bao Huynh, Moazam Bidaki, Amir Masoud Rahmani, Mehdi Hosseinzadeh, Mohammad Masdari:Optimized fuzzy clustering using moth-flame optimization algorithm in wireless sensor networks. Artif. Intell. Rev. 55(3): 1915-1945 (2022). https://doi.org/10.1007/s10462-021-09957-3

[14] Thom Castermans, Bettina Speckmann, Frank Staals, Kevin Verbeek:Agglomerative Clustering of Growing Squares. Algorithmica 84(1): 216-233 (2022). https://doi.org/10.1007/s00453-021-00873-0

[15] Adrian Dumitrescu, Csaba D. Tóth:Online Unit Clustering and Unit Covering in Higher Dimensions. Algorithmica 84(5): 1213-1231 (2022). https://doi.org/10.1007/s00453-021-00916-6

[16] Arvinder Kaur, Yugal Kumar:Neighborhood search based improved bat algorithm for data clustering. Appl. Intell. 52(9): 10541-10575 (2022). https://doi.org/10.1007/s10489-021-02934-x

[17] Dinh Phamtoan, Khanh Nguyenhuu, Tai Vovan:Fuzzy clustering algorithm for outlier-interval data based on the robust exponent distance. Appl. Intell. 52(6): 6276-6291 (2022). https://doi.org/10.1007/s10489-021-02773-w

[18] Mahyar Sadrishojaei, Nima Jafari Navimipour, Midia Reshadi, Mehdi Hosseinzadeh:A new clustering-based routing method in the mobile internet of things using a krill herd algorithm. Clust. Comput. 25(1): 351-361 (2022). https://doi.org/10.1007/s10586-021-03394-1