Ansel Y. Rodríguez-González
Ramón Aranda
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Abstract
Frequent similar pattern mining (FSP mining) allows for finding frequent patterns hidden from the classical approach. However, the use of similarity functions implies more computational effort, necessitating the development of more efficient algorithms for FSP mining. This work aims to improve the efficiency 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 mining all FSPs 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 mining all FSPs using Boolean and non-increasing monotonic similarity functions.
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- X-FSPMiner: A Novel Algorithm for Frequent Similar Pattern Mining
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