A Parallel FP-Growth Mining Algorithm with Load Balancing Constraints for Traffic Crash Data

Traffic safety is an important part of the roadway in sustainable development. Freeway traffic crashes typically cause serious casualties and property losses, being a serious threat to public safety. Figuring out the potential correlation between various risk factors and revealing their coupling mechanisms are of effective ways to explore and identity freeway crash causes. However, the existing association rule mining algorithms still have some limitations in both efficiency and accuracy. Based on this consideration, using the freeway traffic crash data obtained from WDOT (Washington Department of Transportation), this research constructed a multi-dimensional multilevel system for traffic crash analysis. Considering the load balancing, the FP-Growth (Frequent Pattern- Growth) algorithm was optimized parallelly based on Hadoop platform, to achieve an efficient and accurate association rule mining calculation for massive amounts of traffic crash data; then, according to the results of the coupling mechanism among the crash precursors, the causes of freeway traffic crashes were identified and revealed. The results show that the parallel FPgrowth algorithm with load balancing constraints has a better operating speed than both the conventional FP-growth algorithm and parallel FP-growth algorithm towards processing big data. This improved algorithm makes full use of Hadoop cluster resources and is more suitable for large traffic crash data sets mining while retaining the original advantages of conventional association rule mining algorithm. In addition, the mining association rules model with the improvement of multi-dimensional interaction proposed in this research can catch the occurrence mechanism of freeway traffic crash with serious consequences (lower support degree probably) accurately and efficiently

RESEARCH ISSUES CONCERNING ALGORITHMS USED FOR OPTIMIZING THE DATA MINING PROCESS

In this paper, we depict some of the most widely used data mining algorithms that have an overwhelming utility and influence in the research community. A data mining algorithm can be regarded as a tool that creates a data mining model. After analyzing a set of data, an algorithm searches for specific trends and patterns, then defines the parameters of the mining model based on the results of this analysis. The above defined parameters play a significant role in identifying and extracting actionable patterns and detailed statistics. The most important algorithms within this research refer to topics like clustering, classification, association analysis, statistical learning, link mining. In the following, after a brief description of each algorithm, we analyze its application potential and research issues concerning the optimization of the data mining process. After the presentation of the data mining algorithms, we will depict the most important data mining algorithms included in Microsoft and Oracle software products, useful suggestions and criteria in choosing the most recommended algorithm for solving a mentioned task, advantages offered by these software products.data mining optimization, data mining algorithms, software solutions