Survey performance Improvement FP-Tree Based Algorithms Analysis

Construction of a compact FP-tree ensures that subsequent mining can be performed with a rather compact data structure. For large databases, the research on improving the mining performance and precision is necessary; so many focuses of today on association rule mining are about new mining theories, algorithms and improvement to old methods. Association rules mining is a function of data mining research domain and arise many researchers interest to design a high efficient algorithm to mine association rules from transaction database. Generally the entire frequent item sets discovery from the database in the process of association rule mining shares of larger, these algorithms considered as efficient because of their compact structure and also for less generation of candidates item sets compare to Apriori .the price is also spending more. This paper introduces an improved aprior algorithm so called FP-growth algorithm

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

HYBRID: an efficient unifying process to mine frequent itemsets

Current advancement in technology inexorably leads to data flood. More data is generated from banking, telecom, scientific experiments, etc. Data mining is the process of extracting useful information from this flooded data, which helps in making profitable future decisions in these fields. Frequent itemset mining is one of the focus research areas and an important step to fin association rules. Time and space requirements for generating frequent itemsets are of utter importance. Algorithms to mine frequent itemsets effectively help in finding association rules and also help in many other data mining tasks. In this paper, an efficient hybrid algorithm was designed using a unifying process of the algorithms Improved Apriori and FP-Growth. Results indicate that the proposed hybrid algorithm, albeit more complex, consumes fewer memory resources and faster execution time