Neural Processing Letters

, Volume 46, Issue 2, pp 379–409 | Cite as

A New Evolving Tree-Based Model with Local Re-learning for Document Clustering and Visualization

  • Wui Lee Chang
  • Kai Meng Tay
  • Chee Peng Lim


The Evolving tree (ETree) is a hierarchical clustering and visualization model that allows the number of clusters to grow and evolve with new data samples in an online learning manner. While many hierarchical clustering models are available in the literature, ETree stands out because of its visualization capability. It is an enhancement of the Self-Organizing Map, a famous and useful clustering and visualization model. ETree organises the trained data samples in the form of a tree structure for better presentation and visualization especially for high-dimensional data samples. Even though ETree has been used in a number of applications, its use in textual document clustering and visualization is limited. In this paper, ETree is modified and deployed as a useful model for undertaking textual documents clustering and visualization problems. We introduce a new local re-learning procedure that allows the tree structure to grow and adapt to new features, i.e., new words from new textual documents. The performance of the proposed ETree model is evaluated with two (one benchmark and one real) document data sets. A number of key aspects of the proposed ETree model, which include its topology representation, learning time, as well as recall and precision rates, are evaluated. The results show that the proposed local re-learning procedure is useful for handling increasing number of features incrementally. In summary, this study contributes towards a modified ETree model and its use in a new domain, i.e., textual document clustering and visualization.


Evolving tree Textual documents Clustering Visualization Local re-learning 



To 2nd Regional Engineering Conference 2008 (EnCon 2008), and the organizing committee. Special thanks to Miss Liew Hui Chang who had helped during information collections and compilations. The authors had the permission to use the collection of abstracts from EnCon 2008, in which the authors would like to express gratitude for.


  1. 1.
    Rui X, Wunsch DC (2009) Clustering. Wiley, IEEE PressGoogle Scholar
  2. 2.
    Kohonen T (2001) Self-organizing maps, 3rd edn. Springer, BerlinCrossRefzbMATHGoogle Scholar
  3. 3.
    Kohonen T (1990) The self-organizing map. Proc IEEE 78(9):1464–1480CrossRefGoogle Scholar
  4. 4.
    Rauber A, Merkl D, Dittenbachm M (2002) The growing hierarchical self-organizing map: exploratory analysis of high-dimensional data. IEEE Trans Neural Netw 13(6):1331–1341CrossRefGoogle Scholar
  5. 5.
    Carpenter GA, Grossberg S, Rosen DB (1991) ART 2-A: an adaptive resonance algorithm for rapid category learning and recognition. Neural Netw 4:493–504CrossRefGoogle Scholar
  6. 6.
    Carpenter GA, Grossberg S, Markuzon N, Reynolds JH, Rosen DB (1992) Fuzzy ARTMAP: a neural network architecture for incremental supervised learning of analog multidimensional maps. IEEE Trans Neural Netw 3(5):698–713CrossRefGoogle Scholar
  7. 7.
    Pal NR, Pal K, Keller JM, Bezdek JC (2005) A possibilistic fuzzy c-means clustering algorithm. IEEE Trans Fuzzy Syst 13(4):517–530CrossRefGoogle Scholar
  8. 8.
    Kanungo T, Mount DM, Nethanyahu NS, Piatko CD, Silverman R, Wu AY (2002) An efficient k-means clustering algorithm: analysis and implementation. IEEE Trans Pattern Anal Mach Intell 24(7):881–892CrossRefGoogle Scholar
  9. 9.
    Xu C, Tao D, Xu C (2015) Multi-view self-paced learning for clustering. In: Proceedings of 24th international conference on artificial intelligence, pp 3974–3980Google Scholar
  10. 10.
    Arora R, Gupta MR, Kapila A, Fazel M (2013) Similarity-based clustering by left-stochastic matrix factorization. Mach Learn Res 14(1):1715–1746MathSciNetzbMATHGoogle Scholar
  11. 11.
    Hsu CC, Lin SH, Tai WS (2011) Apply extended self-organizing map to cluster and classify mixed-type data. Neurocomputing 74(18):3832–3842CrossRefGoogle Scholar
  12. 12.
    Tai WS, Hsu CC, Chen JC (2010) A mixed-type self-organizing map with a dynamic structure. In: International conference on neural networks, pp 1–8Google Scholar
  13. 13.
    Matharage S, Alahakoon D, Rajapakse J, Huang P (2011) Fast growing self-organizing map for text clustering. In: Lecturer notes computer science, neural information processing, 7063, pp 406–415Google Scholar
  14. 14.
    Kuo RJ, Wang CF, Chen ZY (2012) Integration of growing self-organizing and continuous genetic algorithm for grading lithium-ion battery cells. Appl Soft Comput 8(12):2012–2022CrossRefGoogle Scholar
  15. 15.
    Huang SY, Tsaih RH (2012) The prediction approach with growing hierarchical self-organizing map. In: International conference on neural networks, pp 1–7Google Scholar
  16. 16.
    Hosseini HS (2011) Binary tree time adaptive self-organizing map. Neurocomputing 74(11):1823–1839MathSciNetCrossRefGoogle Scholar
  17. 17.
    Allahyar A, Yazdi HS, Harati A (2015) Constrained semi-supervised growing self-organizing map. Neurocomputing 147:456–471CrossRefGoogle Scholar
  18. 18.
    Pakkanen J, Iivarinen J, Oja E (2006) The evolving tree-analysis and applications. IEEE Trans Neural Netw 17(3):591–603CrossRefGoogle Scholar
  19. 19.
    Pakkanen J, Iivarinen J, Oja E (2004) The evolving tree: a novel self-organizing network for data analysis. Neural Process Lett 20(33):199–211CrossRefGoogle Scholar
  20. 20.
    Fabrizio S (2005) Text cetegorization. In: Alessandro Z (ed) Text mining and its applications. WIT Press, Southampton, pp 109–129Google Scholar
  21. 21.
    Fabrizio S (2002) Machine learning in automated text categorization. ACM Comput Surv 34(1):1–47CrossRefGoogle Scholar
  22. 22.
    Lagus K, Kaski S, Kohonen T (2004) Mining massive document collections by the WEBSOM method. Inf Sci 163(1):135–156CrossRefGoogle Scholar
  23. 23.
    Kaski S, Honkela T, Lagus K, Kohonen T (1998) WEBSOM: self-organizing maps of document collections. Neurocomputing 21(1):101–117CrossRefzbMATHGoogle Scholar
  24. 24.
    Lewis DD (1998) Naïve Bayes at forty: the independence as assumption in information retrieval. Lect Notes Comp Sci 1398:4–15CrossRefGoogle Scholar
  25. 25.
    Hotho A, Maedche A, Staab S (2002) Ontology-based text document clustering. KI 16(4):48–54Google Scholar
  26. 26.
    Witten IH, Frank E, Hall MA (2011) Data mining: practical machine learning tools and techniques, 3rd edn. Morgan Kaufmann, BurlingtonGoogle Scholar
  27. 27.
    Dhillon IS (2001) Co-clustering documents and words using bipartite spectral graph partitioning. In: Proceedings of 7th international conference on knowledge discovery data mining, pp 269–274Google Scholar
  28. 28.
    Liu Y, Loh HT, Sun A (2009) Imbalanced text classification: a term weighting approach. Expert Syst Appl 36(1):690–701CrossRefGoogle Scholar
  29. 29.
    Wold S, Esbensen K, Geladi P (1987) Principal component analysis. Chemom Intell Lab Syst 2(1–3):37–52CrossRefGoogle Scholar
  30. 30.
    Ye J, Li Q (2004) LDA/QR: an efficient and effective dimension reduction algorithm and its theoretical foundation. Pattern Recognit 37(4):851–854CrossRefGoogle Scholar
  31. 31.
    Roweis ST, Saul LK (2000) Nonlinear dimensionality reduction by locally linear embedding. Science 290(5500):2323–2326CrossRefGoogle Scholar
  32. 32.
    Belkin M, Niyogi P (2003) Laplacian eigenmaps for dimensionality reduction and data representation. Neural Comput 15(6):1373–1396CrossRefzbMATHGoogle Scholar
  33. 33.
    Yu J, Tao D, Wang M (2012) Adaptive hypergraph learning and its application in image classification. IEEE Trans Image Process 21(7):3262–3272MathSciNetCrossRefGoogle Scholar
  34. 34.
    Yu J, Hong R, Wang M, You J (2014) Image clustering based on sparse patch alignment framework. Pattern Recognit 47(11):3512–3519CrossRefGoogle Scholar
  35. 35.
    Tao D, Tang X, Li X, Wu X (2006) Asymmetric bagging and random subspace for support vector machines-based relevance feedback in image retrieval. IEEE Trans Pattern Anal Mach Intell 28(7):1088–1099CrossRefGoogle Scholar
  36. 36.
    Yu J, Rui Y, Tao D (2014) Click prediction for web image reranking using multimodal sparse coding. IEEE Trans Image Process 23(5):2019–2032MathSciNetCrossRefGoogle Scholar
  37. 37.
    Tao D, Li X, Wu X, Maybank SJ (2007) General tensor discriminant analysis and gabor features for gait recognition. IEEE Trans Pattern Anal Mach Intell 29(10):1700–1715CrossRefGoogle Scholar
  38. 38.
    Luo Y, Tao D, Ramamohanarao K, Xu C, Wen Y (2015) Tensor canonical correlation analysis for multi-view dimension reduction. IEEE Trans Knowl Data Eng 27(11):3111–3124CrossRefGoogle Scholar
  39. 39.
    Luo Y, Tang J, Yan J, Xu C, Chen Z (2014) Pre-trained multi-view word embedding using two-side neural network. In: Proceedings of 28th AAAI conference, pp 1982–1988Google Scholar
  40. 40.
    Moore BC (1981) Principle component analysis in linear systems: controllability, observability, and model reduction. IEEE Trans Automat Control 26(1):17–32MathSciNetCrossRefzbMATHGoogle Scholar
  41. 41.
    Bingham E, Mannila H (2001) Random projection in dimensionality reduction: applications to image and text data. In: Proceedings of 7th international conference on knowledge discovery data mining, pp 245–250Google Scholar
  42. 42.
    Sammon JW (1969) A nonlinear mapping for data structure analysis. IEEE Trans Comput 18(5):401–409CrossRefGoogle Scholar
  43. 43.
    Kohonen T, Kaski S, Lagus K, Salojarvi J, Honkela J, Paatero V, Saarela A (2000) Self organization of a massive document collection. IEEE Trans Neural Netw 11(3):574–586CrossRefGoogle Scholar
  44. 44.
    Bourgeois N, Cottrell M, Deruelle B, Lamasse S, Letremy P (2015) How to improve robustness in Kohonen maps and display additional information in factorial analysis: application to text mining. Neurocomputing 147:120–135CrossRefGoogle Scholar
  45. 45.
    Liu Y, Wang X, Wu C (2008) ConSOM: a conceptional self-organizing map model for text clustering. Neurocomputing 71(4):857–862CrossRefGoogle Scholar
  46. 46.
    Lughofer E (2011) Evolving fuzzy systems-methodologies, advanced concepts and applications, 1st edn. Springer, BerlinCrossRefzbMATHGoogle Scholar
  47. 47.
    Kim HJ, Kim JU, Ra YG (2005) Boosting Naïve Bayes text classification using uncertainty-based selective sampling. Neurocomputing 67(4):403–410CrossRefGoogle Scholar
  48. 48.
    Pan SJ, Yang Q (2010) A survey on transfer learning. IEEE Trans Knowl Data Eng 22(10):1345–1359CrossRefGoogle Scholar
  49. 49.
    Bezdek JC, Keller J, Krisnapuram R, Pal NR (1999) Fuzzy models and algorithms for pattern recognition and image processing. Kluwer, DordrechtCrossRefzbMATHGoogle Scholar
  50. 50.
    Chang WL, Tay KM, Lim CP (2014) A new evolving tree for text document clustering and visualization. In: Soft computing in industrial applications, Springer, pp 141–151Google Scholar
  51. 51.
    Chang WL, Tay KM, Lim CP (2013) Enhancing an evolving tree-based text document visualization model with fuzzy \(c\)-means clustering. In: IEEE international conference fuzzy, pp 1–6Google Scholar
  52. 52.
    The Reuters-21578, Distribution 1.0 test collection is available from
  53. 53.
    Porter MF (1980) An algorithm for suffix stripping. Program Electron Lib 14(3):130–137Google Scholar
  54. 54.
    The Default English Stop-words List is available from
  55. 55.
    Debole F, Sebastiani F (2005) An analysis of the relative hardness of Rueters-21578 subsets. J Am Soc Inf Sci Technol 56(6):584–586CrossRefGoogle Scholar
  56. 56.
    Yang Y, Liu X (1999) A re-examination of text categorization methods. In: Proceedings of the 22nd annual international ACM SIGIR conference on Research and development in information retrieval, pp 42–49Google Scholar
  57. 57.
    King A (2012) Online k-means clustering of nonstationary data. Prediction Project ReportGoogle Scholar
  58. 58.
    Lin YS, Jiang JY, Lee SJ (2014) A similarity measure for text classification and clustering. IEEE Trans Knowl Data Eng 26(7):1575–1590CrossRefGoogle Scholar
  59. 59.
    Nagwani NK (2015) A comment on “a similarity measure for text classification and clustering”. IEEE Trans Knowl Data Eng 27(9):2589–2590CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media New York 2017

Authors and Affiliations

  1. 1.Faculty of EngineeringUniversiti Malaysia SarawakKota SamarahanMalaysia
  2. 2.Institute for Intelligent Systems Research and InnovationDeakin UniversityGeelongAustralia

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