Open AccessMining of the Correlations for Fatal Road Accident using Graph-based Fuzzified FP-Growth Algorithm
Author(s) -
Soniya Mudgal,
Mahesh Parmar
Publication year - 2020
Publication title -
international journal of engineering and advanced technology
Language(s) - English
Resource type - Journals
ISSN - 2249-8958
DOI - 10.35940/ijeat.e9526.069520
Subject(s) - support vector machine , association rule learning , cluster analysis , algorithm , traffic accident , computer science , road accident , population , accident (philosophy) , data mining , artificial intelligence , medicine , engineering , forensic engineering , transport engineering , environmental health , philosophy , epistemology
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.