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
Xu, R., Ii: Survey of clustering algorithms. IEEE Transactions on Neural Networks 16(3), 645–678 (2005)
Becker, B., Kohavi, R., Sommerfield, D.: Visualizing the simple Bayesian classifier. In: Information Visualization in Data Mining and Knowledge Discovery, pp. 237–249 (2001)
Weinstein, M., Horn, D.: Dynamic quantum clustering: A method for visual exploration of structures in data. Phys. Rev. E 80(6), 066117 (2009)
Premalatha, K., Natarajan, A.M.: A literature review on document clustering. Information Technology Journal 9(5), 993–1002 (2010)
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)
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)
Salton, G.: The SMART Retrieval System—Experiments in Automatic Document Processing. Prentice-Hall, Inc., Upper Saddle River (1971)
Jardine, N., van Rijsbergen, C.J.: The use of hierarchic clustering in information retrieval. Information Storage and Retrieval 7(5), 217–240 (1971)
Nasios, N., Bors, A.G.: Kernel-based classification using quantum mechanics. Pattern Recognition 40, 875–889 (2007)
Robertson, S.E., Jones, K.S.: Relevance weighting of search terms. Journal of the American Society for Information Science 27(3), 129–146 (1976)
Horn, D., Gottlieb, A.: Algorithm for data clustering in pattern recognition problems based on quantum mechanics. Phys. Rev. Lett. 88(1), 018702 (2001)
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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
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