Issue No. 04 - April (2011 vol. 23)
DOI Bookmark: http://doi.ieeecomputersociety.org/10.1109/TKDE.2010.144
Renchu Guan , Jilin University, Changchun
Xiaohu Shi , Jilin University, Changchun
Maurizio Marchese , University of Trento, Trento
Chen Yang , Jilin University, Changchun
Yanchun Liang , Jilin University, Changchun
Based on an effective clustering algorithm—Affinity Propagation (AP)—we present in this paper a novel semisupervised text clustering algorithm, called Seeds Affinity Propagation (SAP). There are two main contributions in our approach: 1) a new similarity metric that captures the structural information of texts, and 2) a novel seed construction method to improve the semisupervised clustering process. To study the performance of the new algorithm, we applied it to the benchmark data set Reuters-21578 and compared it to two state-of-the-art clustering algorithms, namely, k-means algorithm and the original AP algorithm. Furthermore, we have analyzed the individual impact of the two proposed contributions. Results show that the proposed similarity metric is more effective in text clustering (F-measures ca. 21 percent higher than in the AP algorithm) and the proposed semisupervised strategy achieves both better clustering results and faster convergence (using only 76 percent iterations of the original AP). The complete SAP algorithm obtains higher F-measure (ca. 40 percent improvement over k-means and AP) and lower entropy (ca. 28 percent decrease over k-means and AP), improves significantly clustering execution time (20 times faster) in respect that k-means, and provides enhanced robustness compared with all other methods.
Affinity propagation, text clustering, cofeature set, unilateral feature set, significant cofeature set.
X. Shi, C. Yang, M. Marchese, Y. Liang and R. Guan, "Text Clustering with Seeds Affinity Propagation," in IEEE Transactions on Knowledge & Data Engineering, vol. 23, no. , pp. 627-637, 2010.