Advertisement

Clustering Ensemble for Categorical Geological Text Based on Diversity and Quality

  • Hongling WangEmail author
  • Yueshun He
  • Ping Du
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 905)

Abstract

Clustering analysis for geological text makes the navigation, retrieval or extraction of geological text more effectively. Clustering ensemble can be employed to obtain more robust clustering results. However, most generation approaches focus on the diversity of clustering members rather than their quality. Too much emphasis on the diversity of clustering members reduces the accuracy of clustering results. In order to solve the problem, a new generation method of clustering members is proposed in this paper. Hierarchical clustering algorithm and k-means algorithm alternately combined with random projection method are employed to generate diverse base members and a new selection strategy for the number of clusters is presented to improve the quality of clustering members. Furthermore, a clustering ensemble framework for geological text is constructed. The framework involves geological text preprocessing, geological text feature representation, clustering members generation and ensemble integration. Experimental results on two UCI datasets and one real-world geological text demonstrate that the clustering ensemble based on diversity and quality is superior to those clustering ensemble algorithms that only consider the diversity of clustering members.

Keywords

Clustering ensemble Diversity Geological text Quality 

Notes

Acknowledgement

This study is supported in part by National Natural Science Foundation of China (No. 41802247, 41862012), Open Fund of Jiangxi Engineering Laboratory on Radioactive Geoscience and Big data Technology (No. JELRGBDT201708, No. JELRGBDT201705), Key Research Development Foundation of Jiangxi Province Technology Department (No. 20161BBE50063).

References

  1. 1.
    Alizadeh, H., Yousefnezhad, M., Bidgoli, B.M.: Wisdom of crowds cluster ensemble. Intell. Data Anal. 19, 485–503 (2015)CrossRefGoogle Scholar
  2. 2.
    Huang, D., Lai, J.H., Wang, C.D.: Combining multiple clustering via crowed agreement estimation and multi-granularity link analysis. Neurocomputing 170, 240–250 (2015)CrossRefGoogle Scholar
  3. 3.
    Strehl, A., Ghosh, J.: Cluster ensemble-a knowledge reuse framework for combining multiple partitions. In: Proceedings of the 18th National Conference on Artificial Intelligence, pp. 93–98 (2002)Google Scholar
  4. 4.
    Jain, A.K., Topchy, A., Law, M.H., Buhmann, J.M.: Landscape of clustering algorithms. In: Proceedings of the 17th International Conference on Pattern Recognition, pp. 260–263 (2004)Google Scholar
  5. 5.
    Minaei-Bidgoli, B., Topchy, A., Punch, W.F.: Ensembles of partitions via data resampling. In: International Conference on Information Technology: Coding and Computing, p. 188 (2004)Google Scholar
  6. 6.
    Dudoit, S., Fridlyand, J.: Bagging to improve the accuracy of a clustering procedure. Bioinformatics 19(9), 1090–1099 (2003)CrossRefGoogle Scholar
  7. 7.
    Topchy, A., Jain, A.K., Punch, W.: Combining multiple weak clusterings. In: Proceedings of the Third IEEE International Conference on Data Mining, pp. 331–338 (2003)Google Scholar
  8. 8.
    Fern, X.Z., Brodley, C.E.: Random projection for high dimensional data clustering: a cluster ensemble approach. In: Twentieth International Conference on Machine Learning, pp. 186–193 (2003)Google Scholar
  9. 9.
    Hadjitodorov, S.T., Kuncheva, L.I., Todorova, L.P.: Moderate diversity for better cluster ensembles. Inf. Fusion 7(3), 264–275 (2006)CrossRefGoogle Scholar
  10. 10.
    de Amorim, R.C., Hennig, C.: Recovering the number of clusters in data sets with noise features using feature rescaling factors. Inf. Sci. 324, 126–145 (2015)MathSciNetCrossRefGoogle Scholar
  11. 11.
    Meng, J., Hao, H., Luan, Y.S.: Classifier ensemble selection based on affinity propagation clustering. J. Biomed. Inform. 60, 234–242 (2016)CrossRefGoogle Scholar
  12. 12.
    Yu, Z.W., Li, L., Gao, Y.J., You, J., Liu, J.M., Wong, H.S., Han, G.Q.: Hybrid clustering solution selection strategy. Pattern Recognit. 47(10), 3362–3375 (2014)CrossRefGoogle Scholar
  13. 13.
    Yousefnezhad, M., Reihanian, A., Zhang, D.Q., Minaei-Bidgoli, B.: A new selection strategy for selective cluster ensemble based on Diversity and Independency. Eng. Appl. Artif. Intell. 56, 260–272 (2016)CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  1. 1.East China University of TechnologyNanchangChina
  2. 2.Jiangxi Engineering Laboratory on Radioactive Geoscience and Big Data TechnologyEast China University of TechnologyNanchangChina
  3. 3.China University of GeosciencesWuhanChina

Personalised recommendations