Applications of FP-Growth and Apriori Algorithm for Mining Fuzzified Spatial Dataset
Puneet Matapurkar1, Saurabh Shrivastava2
1Puneet Matapurkar*, Department of Mathematical Sciences and Computer Applications, Bundelkhand university, Jhansi (Uttar Pradesh) India.
2Dr. Saurabh Shrivastava, Department of Mathematical Sciences and Computer Applications, Bundelkhand university, Jhansi (Uttar Pradesh) India.
Manuscript received on November 22, 2019. | Revised Manuscript received on December 15, 2019. | Manuscript published on December 30, 2019. | PP: 2405-2411 | Volume-9 Issue-2, December, 2019. | Retrieval Number: B3866129219/2019©BEIESP | DOI: 10.35940/ijeat.B3866.129219
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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, also called geospatial data, is term needed to describe data linked to or containing knowledgeable data about a particular location on Earth’s surface. Spatial data mining’s primary goal is to uncover hidden complicated information from spatial & non-spatial information in spite of their enormous quantity and find the spatial relations density. Spatial Data Mining techniques, however, continue to be an expansion of individuals utilized in standard data mining. Spatial Data is an extremely challenging area since enormous quantities of spatial data have been obtained from the remote sensed to the GIS (Geographic Information Systems), ecological estimation, computer cartography, planning and many more. In a given paper, we only focus on an essential type of spatial vagueness termed as spatial fuzziness. Spatial fuzziness intakes the property of several spatial objects in certainty which don’t contain boundaries of sharp type and interiors or whose boundaries as well as interiors can’t be determined in precise form. This paper provides the method for finding fuzzy spatial data of association rule. Association rules provided valuable data in the assessment of important correlations observed in big databases. Compared to the previous research work, the current approach for there search highlights the superiority over the same dataset in terms of time taken and generated rules. The rules generated tell about the occurrence of attributes. The results show that the current research is more efficient than that of the previous work and also less time-consuming.
Keywords: Data Mining, Spatial Data Mining (SDM), Association Rule Mining (ARM), Apriori, FP-Growth, Spatial Data, Fire Data.