Abstract
China is experiencing a serious aging crisis, with the number of elderly people rising at an unprecedented rate. First and foremost, the pension industry's service object is the pension. To build an old-age industry complex, make use of the unique natural resources in the countryside, provide the original ecological old-age care services, and transform, optimize, and make efficient use of the existing policies and technologies. The drawbacks of the traditional pension model are becoming increasingly apparent. We must investigate a new pension model in order to ensure the long-term development of China's pension system and reduce the increased pension burden on governments at all levels. The rural areas' unique natural resources should be utilized to provide unique ecological elderly care services, and transformation and optimization must be implemented (that is, making efficient use of existing policies and technology to construct an elderly care industry complex). As a new pension mode for the elderly to become more connected to the natural world and improve their physical and mental health, ecological pensions have emerged as a new economic growth point that can fill the gap in the pension industry's supply while also meeting the elderly's growing spiritual and cultural needs. A constrained clustering algorithm is proposed in this article. Unsupervised learning is used in the constrained clustering algorithm. The clustering algorithm must determine the data objects to be clustered because they are not labeled. Because the data objects have no prior knowledge, the clustering algorithm analyzes them using the same principles. The effectiveness of the clustering results is determined by the dataset's adherence to the previously stated principles. The constrained clustering algorithm has greatly improved the transformation and optimization of the rural ecological elderly care industrial chain.
Highlights
With the deepening of China’s aging population, the pension industry has gradually become a new sunrise industry, with an immeasurable development prospect [1]
In literature [27], through the big data analysis method, China has been involved in a serious wave of aging, the number of the elderly population is increasing at an unprecedented rate, and the existing elderly care institutions are far from the actual elderly care service expectations of the people in terms of number and service quality
Is article studies the effectiveness and importance of constrained clustering algorithms in the transformation and optimization of rural ecological elderly care industrial chains. e choice of ecological pension industry model is one of the frontier issues in the economic theory research of the pension industry, and the research on the implementation path of rural revitalization is the main focus of the development theory research of “agriculture, rural areas, and farmers.”
Summary
With the deepening of China’s aging population, the pension industry has gradually become a new sunrise industry, with an immeasurable development prospect [1]. E existing old-age care institutions are far below the actual expectations of the people in terms of number and service quality [2]. Unlike classification [9,10,11], clustering usually has no prior knowledge or background knowledge as guidance It is an automatic recognition [12] and unsupervised learning process based on object similarity [13]. Because of the use of domain knowledge, the clustering algorithm can obtain more heuristic information, reducing the “blindness” in its search process and improving the efficiency and clustering quality. By selecting the initial center point, the potential hierarchical structure information of the system can be extracted, which can serve as a heuristic for subsequent clustering [22]
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