Efficient algorithms for mining top-rank-k erasable patterns using pruning strategies and the subsume concept

Mining erasable patterns (EPs) is one of the emerging tasks in data mining which helps factory managers to establish plans for the development of new systems of products. However, systems usually face the problem of many EPs. Therefore, the problem of mining top-rank-k EPs, and an algorithm for mining these using the PID_List structure named VM, were proposed in 2013. In this paper, we propose two efficient methods, named the TEP (Top-rank-k Erasable Pattern mining) and TEPUS (Top-rank-k Erasable Pattern mining Using the Subsume concept) algorithms, for mining top-rank-k EPs. The TEP algorithm uses the dPidset structure to reduce the memory usage and a dynamic threshold pruning strategy to accelerate the mining process. The TEPUS algorithm is the extension of the TEP algorithm using the subsume concept and the index strategy to further speed up the mining time and reduce the memory usage. Finally, we conduct an experiment to compare the mining time, memory usage and scalability of TEP, TEPUS and two state-of-the-art algorithms (VM and dVM) for mining top-rank-k EPs. Our performance studies show that TEPUS outperforms TEP, VM and dVM. (C) 2017 Elsevier Ltd. All rights reserved.