X-FSPMiner: A Novel Algorithm for Frequent Similar Pattern Mining

Abstract

Frequent similar pattern mining (FSP mining) allows found frequent patterns hidden from the classical approach. However, the use of similarity functions implies more computational efort, becoming necessary to develop more eicient algorithms for FSP mining. This work aims to improve the eiciency of mining all FSPs when using Boolean and non-increasing monotonic similarity functions. A data structure to condense an object description collection named FV-Tree, and an algorithm for mine all FSP from the FV-Tree, named X-FSPMiner, are proposed. The experimental results reveal that the novel algorithm X-FSPMiner vastly outperforms the state-of-the-art algorithms for mine all FSP using Boolean and non-increasing monotonic similarity functions.