A related degree-based frequent pattern mining algorithm for railway fault data

Abstract

It is of great significance to improve the efficiency of railway production and operation by realizing the fault knowledge association through the efficient data mining algorithm. However, high utility quantitative frequent pattern mining algorithms in the field of data mining still suffer from the problems of low time-memory performance and are not easy to scale up. In the context of such needs, we propose a related degree-based frequent pattern mining algorithm, named Related High Utility Quantitative Item set Mining (RHUQI-Miner), to enable the effective mining of railway fault data. The algorithm constructs the item-related degree structure of fault data and gives a pruning optimization strategy to find frequent patterns with higher related degrees, reducing redundancy and invalid frequent patterns. Subsequently, it uses the fixed pattern length strategy to modify the utility information of the item in the mining process so that the algorithm can control the length of the output frequent pattern according to the actual data situation and further improve the performance and practicability of the algorithm. The experimental results on the real fault dataset show that RHUQI-Miner can effectively reduce the time and memory consumption in the mining process, thus providing data support for differentiated and precise maintenance strategies.

Keywords

• High utility
• Quantitative
• Frequent pattern mining
• Related degree pruning
• Fixed pattern length