LAUSR

Discovering Erasable Patterns (EPs) consists of identifying product parts that will produce a small profit loss if their production is stopped. It is a data mining problem that has attracted the attention of numerous researchers in recent years due to the possibility of using EPs to reduce profit loss of manufacturers. Though, many algorithms have been designed to mine EPs, an important limitation of state-of-the-art EP mining algorithms is that they are batch algorithms, that is, they are designed to be applied on static databases. But in real-life applications, databases are dynamic, as they are constantly updated by adding or removing products and parts. To be informed about EPs in real-time, traditional EP mining algorithms must be applied over and over again on a database. This is inefficient as those algorithms are always applied from scratch without taking advantage of results generated by previous executions. Considering this important drawback of previous work for handling real-life dynamic data, this paper proposes an efficient algorithm named MSPPC for mining EPs in data streams. It relies on a novel tree structure named SPPC (Streaming Pre-Post Code) tree, which extends the WPPC tree structure for maintaining a compact tree representation of EPs in a data stream. Experimental results show that the designed MSPPC algorithm outperforms the state-of-the-art batch MERIT and dMERIT algorithms when they are run in batch mode using a sliding-window. Besides, the proposed algorithm is also faster than the state-of-the-art algorithms for mining EPs, namely MERIT, dMERIT + , MEI and EIFDD.