Ansel Y. Rodríguez-González, Ramón Aranda, Miguel Á. Álvarez-Carmona, Angel Díaz-Pacheco, Rosa María Valdovinos Rosas
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引用次数: 0
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 effort, becoming necessary to develop 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 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.
期刊介绍：
TKDD welcomes papers on a full range of research in the knowledge discovery and analysis of diverse forms of data. Such subjects include, but are not limited to: scalable and effective algorithms for data mining and big data analysis, mining brain networks, mining data streams, mining multi-media data, mining high-dimensional data, mining text, Web, and semi-structured data, mining spatial and temporal data, data mining for community generation, social network analysis, and graph structured data, security and privacy issues in data mining, visual, interactive and online data mining, pre-processing and post-processing for data mining, robust and scalable statistical methods, data mining languages, foundations of data mining, KDD framework and process, and novel applications and infrastructures exploiting data mining technology including massively parallel processing and cloud computing platforms. TKDD encourages papers that explore the above subjects in the context of large distributed networks of computers, parallel or multiprocessing computers, or new data devices. TKDD also encourages papers that describe emerging data mining applications that cannot be satisfied by the current data mining technology.
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