Bounded Memory Based Frequent Pattern Growth Approach With Deep Neural Network and Decision Tree for Road Accident Prediction

Abstract

Frequent Pattern-growth (FP-growth) was introduced to categorize the accident locations. After the categorization of accident locations, accidents were classified by using J48. FP-growth has poor spatial and temporal locality issues. This is because of the construction process of FP-tree and its access behaviour of the mining algorithm. In this article, the issues of FP-growth are solved by introducing Bounded FPgrowth. It uses only a bounded portion of the primary memory using specialized memory management. When the tree grows out of the allocated memory, it is forced to be partially saved on secondary memory. The secondary memory is accessed in a block-by-block basis so that both temporal and spatial localities of FP-growth are optimized. After the memory management process, FP-growth is applied to categorize the accident locations. The attributes which have high support values are processed in Deep Neural Network J48 (DNNJ48) for classification of accident type. The analysis of experimental results proved that the proposed Bounded FP-growth-Deep Neural Network with J48 classifier (BFP-growth-DNNJ48) achieved higher accuracy (94%) when compared with existing methods like AdaBoost-SO and TASP-CNN.