Using extended random set to find specific patterns
Contribuinte(s) |
Skowron, Andrzej Dey, Lipika Krasuski, Adam Li, Yuefeng |
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Data(s) |
2014
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Resumo |
With the overwhelming increase in the amount of data on the web and data bases, many text mining techniques have been proposed for mining useful patterns in text documents. Extracting closed sequential patterns using the Pattern Taxonomy Model (PTM) is one of the pruning methods to remove noisy, inconsistent, and redundant patterns. However, PTM model treats each extracted pattern as whole without considering included terms, which could affect the quality of extracted patterns. This paper propose an innovative and effective method that extends the random set to accurately weigh patterns based on their distribution in the documents and their terms distribution in patterns. Then, the proposed approach will find the specific closed sequential patterns (SCSP) based on the new calculated weight. The experimental results on Reuters Corpus Volume 1 (RCV1) data collection and TREC topics show that the proposed method significantly outperforms other state-of-the-art methods in different popular measures. |
Identificador | |
Publicador |
IEEE |
Relação |
DOI:10.1109/WI-IAT.2014.77 Albathan, Mubarak, Li, Yuefeng, & Xu, Yue (2014) Using extended random set to find specific patterns. In Skowron, Andrzej, Dey, Lipika, Krasuski, Adam, & Li, Yuefeng (Eds.) Proceedings of the 2014 IEEE/WIC/ACM International Joint Conference on Web Intelligence (WI) and Intelligent Agent Technologies (IAT) - Volume 2, IEEE, Warsaw, Poland, pp. 30-37. |
Direitos |
Copyright 2014 by IEEE |
Fonte |
School of Electrical Engineering & Computer Science; Science & Engineering Faculty |
Palavras-Chave | #Extended random set #Information retrieval #Select top-k patterns #Specific closed sequential patterns #Text mining |
Tipo |
Conference Paper |