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Envisioning Dynamic Quantum Clustering in Information Retrieval

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Quantum Interaction (QI 2011)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 7052))

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Abstract

Dynamic Quantum Clustering is a recent clustering technique which makes use of Parzen window estimator to construct a potential function whose minima are related to the clusters to be found. The dynamic of the system is computed by means of the Schrödinger differential equation. In this paper, we apply this technique in the context of Information Retrieval to explore its performance in terms of the quality of clusters and the efficiency of the computation. In particular, we want to analyze the clusters produced by using datasets of relevant and non-relevant documents given a topic.

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References

  1. Xu, R., Ii: Survey of clustering algorithms. IEEE Transactions on Neural Networks 16(3), 645–678 (2005)

    Article  Google Scholar 

  2. Becker, B., Kohavi, R., Sommerfield, D.: Visualizing the simple Bayesian classifier. In: Information Visualization in Data Mining and Knowledge Discovery, pp. 237–249 (2001)

    Google Scholar 

  3. Weinstein, M., Horn, D.: Dynamic quantum clustering: A method for visual exploration of structures in data. Phys. Rev. E 80(6), 066117 (2009)

    Article  Google Scholar 

  4. Premalatha, K., Natarajan, A.M.: A literature review on document clustering. Information Technology Journal 9(5), 993–1002 (2010)

    Article  Google Scholar 

  5. Hearst, M.A., Pedersen, J.O.: Reexamining the cluster hypothesis: scatter/gather on retrieval results. In: Proceedings of SIGIR 1996, pp. 76–84. ACM, New York (1996)

    Google Scholar 

  6. Lamprier, S., Amghar, T., Saubion, F., Levrat, B.: Traveling among clusters: a way to reconsider the benefits of the cluster hypothesis. In: Proceedings of SAC 2010, pp. 1774–1780. ACM, New York (2010)

    Google Scholar 

  7. Salton, G.: The SMART Retrieval System—Experiments in Automatic Document Processing. Prentice-Hall, Inc., Upper Saddle River (1971)

    Google Scholar 

  8. Jardine, N., van Rijsbergen, C.J.: The use of hierarchic clustering in information retrieval. Information Storage and Retrieval 7(5), 217–240 (1971)

    Article  Google Scholar 

  9. Nasios, N., Bors, A.G.: Kernel-based classification using quantum mechanics. Pattern Recognition 40, 875–889 (2007)

    Article  MATH  Google Scholar 

  10. Robertson, S.E., Jones, K.S.: Relevance weighting of search terms. Journal of the American Society for Information Science 27(3), 129–146 (1976)

    Article  Google Scholar 

  11. Horn, D., Gottlieb, A.: Algorithm for data clustering in pattern recognition problems based on quantum mechanics. Phys. Rev. Lett. 88(1), 018702 (2001)

    Article  Google Scholar 

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© 2011 Springer-Verlag Berlin Heidelberg

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Di Buccio, E., Di Nunzio, G.M. (2011). Envisioning Dynamic Quantum Clustering in Information Retrieval. In: Song, D., Melucci, M., Frommholz, I., Zhang, P., Wang, L., Arafat, S. (eds) Quantum Interaction. QI 2011. Lecture Notes in Computer Science, vol 7052. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-24971-6_22

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  • DOI: https://doi.org/10.1007/978-3-642-24971-6_22

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-24970-9

  • Online ISBN: 978-3-642-24971-6

  • eBook Packages: Computer ScienceComputer Science (R0)

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