Abstract
Discovering a representative set of theme patterns from a large amount of text for interpreting their meaning has always been concerned by researches of both data mining and information retrieval. Recent studies of theme pattern mining have paid close attention to the problem of discovering a set of compatible top-k theme patterns with both high-interestingness and low-redundancy. Since different users have different preferences on interestingness and redundancy, how to measure the attributes of the users’ preferences, and thereby to discover “preferred compatible top-k theme patterns” (PCTTP) is urgent in the field of text mining. In this paper, a novel strategy of discovering PCTTP based on users’ preferences in text mining is proposed. Firstly, an evaluation function of the preferred compatibility between every two theme patterns is presented. Then the preferred compatibilities are archived into a data structure called theme compatibility graph, and a problem called MWSP based on the compatibility graph is proposed to formulate the problem how to discover the PCTTP. Secondly, since MWSP is proved to be a NP-Hard problem, a greedy algorithm, DPCTG, is designed to approximate the optimal solution of MWSP. Thirdly, a quality evaluation model is introduced to measure the compatibility of discovering theme patterns. Empirical studies indicate that a high quality set of PCTTP on four different sub text sets can be obtained from DBLP.
This is a preview of subscription content, log in via an institution to check access.
Preview
Unable to display preview. Download preview PDF.
Similar content being viewed by others
References
Afrati, F.N., Gionis, A., Mannila, H.: Approximating a collection of frequent sets. In: KDD 2004, pp. 8–19 (2004)
Krovetz, R.: Viewing morphology as an inference process. In: Proceedings of SIGIR 1993, pp. 191–202 (1993)
Mei, Q., Xin, D., Cheng, H., Han, J., Zhai, C.: Generating semantic annotations for frequent patterns with context analysis. In: KDD 2006, pp. 337–346 (2006)
Mei, Q., Xin, D., Cheng, H., Han, J., Zhai, C.: Discovering Evolutionary Theme semantic annotations for frequent patterns with context analysis. In: KDD 2005 (2005)
Tzvetkov, P., Yan, X., Han, J.: TSP: Mining top-k closed sequential patterns. Knowledge and Information Systems 7, 438–457 (2005)
Varian, H.: Intermediate Microeconomics: A Modern Approach, 6th edn. W.W. Norton & Company Inc. (2003)
Xin, D., Han, J., Yan, X., Cheng, H.: On compressing frequent patterns. In: KIS 2007 (2007)
Xin, D., Han, J., Yan, X., Cheng, H.: Discovering Redundancy-Aware Top-K Patterns. In: KDD 2006, pp. 314–323 (2006)
Yan, X., Cheng, H., Han, J., Xin, D.: Summarizing Itemset Patterns: A Profile Based Approach. In: KDD 2005, pp. 314–323 (2005)
Yan, X., Han, J., Afshar, R.: Clospan: Mining closed sequential patterns in large datasets. In: SDM 2003, pp. 166–177 (2003)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2009 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Tong, Y., Ma, S., Yu, D., Zhang, Y., Zhao, L., Xu, K. (2009). Discovering Compatible Top-K Theme Patterns from Text Based on Users’ Preferences. In: Chen, H., Yang, C.C., Chau, M., Li, SH. (eds) Intelligence and Security Informatics. PAISI 2009. Lecture Notes in Computer Science, vol 5477. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-01393-5_14
Download citation
DOI: https://doi.org/10.1007/978-3-642-01393-5_14
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-01392-8
Online ISBN: 978-3-642-01393-5
eBook Packages: Computer ScienceComputer Science (R0)