The DOI matches its Crossref record; 23 of the 23 references it cites were found in a registry or an index.
DBSCAN: Past, present and future
Abstract
Data Mining is all about data analysis techniques. It is useful for extracting hidden and interesting patterns from large datasets. Clustering techniques are important when it comes to extracting knowledge from large amount of spatial data collected from various applications including GIS, satellite images, X-ray crystallography, remote sensing and environmental assessment and planning etc. To extract useful pattern from these complex data sources several popular spatial data clustering techniques have been proposed. DBSCAN (Density Based Spatial Clustering of Applications with Noise) is a pioneer density based algorithm. It can discover clusters of any arbitrary shape and size in databases containing even noise and outliers. DBSCAN however are known to have a number of problems such as: (a) it requires user's input to specify parameter values for executing the algorithm; (b) it is prone to dilemma in deciding meaningful clusters from datasets with varying densities; (c) and it incurs certain computational complexity. Many researchers attempted to enhance the basic DBSCAN algorithm, in order to overcome these drawbacks, such as VDBSCAN, FDBSCAN, DD_DBSCAN, and IDBSCAN. In this study, we survey over different variations of DBSCAN algorithms that were proposed so far. These variations are critically evaluated and their limitations are also listed.
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Cites
Finding groups in data: an introduction to cluster analysis
(1991) · Choice Reviews Online · doi:10.5860/choice.28-4558 · Cited by 10,474
Self-Organization and Associative Memory
Kohonen, T. (1989) · Springer Series in Information Sciences · doi:10.1007/978-3-642-88163-3 · Cited by 8,740
Ankerst, M. (1999) · doi:10.1145/304181.304187 · Cited by 3,982
Self-Organization and Associative Memory
Kohonen, T. (1988) · Springer Series in Information Sciences · doi:10.1007/978-3-662-00784-6 · Cited by 3,812
Finding Groups in Data: An Introduction to Cluster Analysis.
Sarle, W. S., Kaufman, L., & Rousseeuw, P. J. (1991) · Journal of the American Statistical Association · doi:10.2307/2290430 · Cited by 3,539
Automatic subspace clustering of high dimensional data for data mining applications
Agrawal, R., Gehrke, J., Gunopulos, D., & Raghavan, P. (1998) · Proceedings of the 1998 ACM SIGMOD international conference on Management of data · doi:10.1145/276304.276314 · Cited by 2,412
Chameleon: hierarchical clustering using dynamic modeling
Karypis, G., Han, E.-H., & Kumar, V. (1999) · Computer · doi:10.1109/2.781637 · Cited by 2,097
Knowledge acquisition via incremental conceptual clustering
Fisher, D. H. (1987) · Machine Learning · doi:10.1007/bf00114265 · Cited by 1,991
CURE: an efficient clustering algorithm for large databases
Guha, S., Rastogi, R., & Shim, K. (1998) · Proceedings of the 1998 ACM SIGMOD international conference on Management of data · doi:10.1145/276304.276312 · Cited by 1,907
Efficient and Effective Clustering Methods for Spatial Data Mining
Ng, R. T., & Han, J. (1994) · Cited by 1,778
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Metadata
- Authors
- Rehman, S. U., Asghar, S., Fong, S., & Sarasvady, S.
- Venue
- The Fifth International Conference on the Applications of Digital Information and Web Technologies (ICADIWT 2014)
- Publisher
- IEEE
- Published
- 2014-02
- Pages
- 232-238
- OpenAlex
- W1976383685
- Cited by
- 606
- References
- 23
- FWCI
- 3.16
- Type
- conference paper
- Language
- en
- Open access
- closed
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