Spatial clustering methods in data mining : A survey
By Jiawei Han, Micheline Kamber, Anthony K. H. Tung. 2001
562 citations recorded by the source corpus.
Subfields: Information Systems
View Paper: https://www.semanticscholar.org/paper/57f80a39ebc4d3500441afc01e580083cae0eaf0
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In Information Systems
Citations in the corpus
- Empirical and Theoretical Comparisons of Selected Criterion Functions for Document Clustering (2004, background): “Two recent surveys on the topics (Jain, Murty, & Flynn, 1999; Han, Kamber, & Tung, 2001 ) offer a comprehensive summary of the different applications and algorithms.”
- Geographic Data Mining and Knowledge Discovery (2001, unknown): “Ideally, KDD should be a human-center process based on the available data, the desired knowledge and the intermediate results obtained during the process (see Adriaans and Zantinge 1996; Brachman and Anand 1996; Fayyad, Piatetsky-Shapiro and Smyth 1996; Matheus, Chan and Piatetsky-Shapiro 1993).”
- A simple and fast algorithm for K-medoids clustering (2009, methodology): “However, PAM has a drawback that it works inefficiently for a large data set due to its time complexity (Han et al., 2001). This is the main motivation of this paper. We are interested in developing a new K-medoids clustering algorithm that should be simple but efficient. There have been some effort”
- A Survey of Clustering Data Mining Techniques (2006, background): “Since density-based algorithms require a metric space, the natural setting for them is spatial data clustering [Han et al. 2001; Kolatch 2001].”
- ST-DBSCAN: An algorithm for clustering spatial-temporal data (2007, background): “Existing density-based clustering algorithms [14] produce meaningful and adequate results under certain conditions, but their results are not satisfactory when clusters of different densities exist.”