This paper examines six distinct clustering methods: k-means clustering, hierarchical clustering, DBS can cluster, density-based clustering, optical flow, and EM algorithm. WEKA, a clustering tool, is used to carry out the implementation and analysis of these clustering techniques. Six different methods' results are shown and compared. Retrieving data from scientific and technical literature via R-tree indexing, our method employs an enhanced k-mean clustering algorithm to build a clustering model. The experiments conducted on university science and technology literature datasets demonstrate the effectiveness of the approach described in this paper. Clustering is a well-known, fundamental data mining task that is used to extract information. However, many researchers have developed and provided a wide variety of clustering algorithms to accommodate the adapted applications for the various domains. Because of this, it is challenging for researchers and practitioners to keep up with the progress being made in clustering algorithm development.

Data Clustering, K-Means Clustering, Hierarchical Clustering, DB Scan Clustering, Density Based Clustering, OPTICS, EM Algorithm