id	author	title	date	pages	extension	mime	words	sentence	flesch	summary	cache	txt
ajst-23620	Fu, Yu; He, Qinghui	Clustering Analysis of Gas Wells in Carbonate Reservoirs	2024	4	.pdf	application/pdf	3272	108	37	Parameters are difficult to control and have a large impact on the clustering effect network clustering STING fast Parameter sensitive, can't handle irregularly distributed data, dimensionality disaster model clustering SOM in the form of a probability Inefficient execution, especially when the number of distributions is large and the amount of data is small fuzzy clustering FCM Works well for clustering data that satisfies a normal distribution The performance of the algorithm depends on the initial clustering center partition partitioning K-means Simple and efficient for large datasets, low time complexity and space complexity Sensitive to noise and outliers Comparing the advantages and disadvantages of different clustering algorithms, K-means algorithm is selected for clustering analysis in this study, which has the advantages of simplicity and efficiency, low time and space complexity, and the number of clusters can be formulated according to the needs of the clusters, with the highest analytical efficiency. An example of clustering by distance between data objects Clustering: Cluster analysis refers to the analytical process of grouping a collection of data objects into multiple classes composed of similar objects.	cache/ajst-23620.pdf	txt/ajst-23620.txt