Mining of the Correlations for Fatal Road Accident using Graph-based Fuzzified FP-Growth Algorithm
Soniya Mudgal1, Mahesh Parmar2
1Soniya Mudgal*, Computer Science and Engineering, Madhav Institute of Technology and Science (MITS), Gwalior, India.
2Mahesh Parmar, Computer Science and Engineering, Madhav Institute of Technology and Science (MITS), Gwalior, India.
Manuscript received on April 11, 2020. | Revised Manuscript received on May 15, 2020. | Manuscript published on June 30, 2020. | PP: 279-283 | Volume-9 Issue-5, June 2020. | Retrieval Number: E9526069520/2020©BEIESP | DOI: 10.35940/ijeat.E9526.069520
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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: Rapid population growth and economic activity have caused a continuous growth of motor vehicles and the increase in population and vehicle traffic injuries is increasing each day. Injury and death traffic accidents lead to not only significant economic losses however too severe mental & physical illness. Social issues have been created by the increasing road accident as a result of death and suffering and death. FP Growth Algorithm, Support Vector Machine (SVM) Cluster classification models and simple C-means clustering Algorithm formed Association laws. Some suggestions for safety driving were made based on data, association guidelines, classification model and obtained clusters. In this paper, we will attempt to address this problem by applying statistical study and FARS fatal accident DM algorithms. The findings suggest that the algorithm proposed is more efficient and faster than the algorithm of the previous research.
Keywords: Spatial data mining, air pollution, association rule mining, Fuzzification, graphical representation.