An Empirical Research on Spatial Data Mining
K. Sivakumar1, A.S. Prakaash2
1K. Sivakumar, Professor, Department of Mathematics, Sathyabama Institute of Science and Technology, Chennai (Tamil Nadu), India.
2A.S. Prakaash, Research Scholar, Department of Mathematics, Sathyabama Institute of Science and Technology, Chennai (Tamil Nadu), India.
Manuscript received on 12 December 2019 | Revised Manuscript received on 23 December 2019 | Manuscript Published on 31 December 2019 | PP: 797-800 | Volume-8 Issue-12S2 October 2019 | Retrieval Number: L113610812S219/2019©BEIESP | DOI: 10.35940/ijitee.L1136.10812S219
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open-access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: Spatial data mining is a process of extracting expertise from large volumes of spatial data collected from different applications such as remote sensing, geographic systems and social networks, etc. The collected spatial data are too difficult for the human to analyze. Recent research focuses on data mining to extend the data mining scope from relational storages to spatial databases. A lot of effort put forth to summarize various spatial based knowledge discovery in data mining such as based on generalization, clustering based, spatial associations based, and approximations and aggregations based knowledge discovery are discussed. The discussion shows that spatial data mining is a promising area of information discovery and can lead to extensive research and many challenging issues.
Keywords: Spatial Data Mining, Clustering, Spatial Associations, Knowledge Discovery, Aggregation.
Scope of the Article: Data Mining