Abstract
Generally, clustering feature tree consists of nodes given as vectors. In case of non-vector nodes a transformation into feature vectors is needed. Feature extraction algorithm determines the volume and quality of information enclosed in features and quality of clustering. Thus this kind of transformation is important part of clustering procedure. In this paper an approach to clustering feature space construction from clustering feature tree is proposed. Presented approach allows to save hierarchy information and reduce feature space dimension. An efficiency of proposed approach is shown in the experiment part with different clustering algorithms. Result analysis is provided at the end of the paper.
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References
Amorim, R.: Feature weighting for clustering: using K-means and the Minkowski. LAP Lambert Academic Publishing (2012)
Ball, G.H., Hall, David J.: Isodata: a method of data analysis and pattern classification, Stanford Research Institute, Menlo Park, United States. Office of Naval Re-search, Information Sciences Branch (1965)
Frey, B.J., Dueck, D.: Clustering by passing messages between data points. Science 315, 972–976 (2007)
Comaniciu, D., Meer, P.: Mean shift: a robust approach toward feature space analysis. IEEE Trans. Pattern Anal. Mach. Intell. 24, 603–619 (2002)
Dudarin, P., Pinkov, A., Yarushkina, N.: Methodology and the algorithm for clustering economic analytics object. Autom. Control. Process. 47(1), 85–93 (2017)
Dudarin, P., Yarushkina, N.: Features construction from hierarchical classifier for short text fragments clustering. Fuzzy Syst. Soft Comput. 12, 87–96 (2018). https://doi.org/10.26456/fssc26
Dudarin, P.V., Yarushkina, N.G.: Algorithm for constructing a hierarchical classifier of short text fragments based on the clustering of a fuzzy graph. Radio Eng. 2017(6), 114–121 (2017)
Dudarin, P.V., Yarushkina, N.G.: An approach to fuzzy hierarchical clustering of short text fragments based on fuzzy graph clustering. In: Abraham, A., Kovalev, S., Tarassov, V., Snasel, V., Vasileva, M., Sukhanov, A. (eds.) IITI 2017. AISC, vol. 679, pp. 295–304. Springer, Cham (2018). https://doi.org/10.1007/978-3-319-68321-8_30
Ester M., Kriegel H. P., SanderJ., Xu X.: A density-based algorithm for discovering clusters in large spatial databases with noise. In: Proceedings of the 2nd International Conference on Knowledge Discovery and Data Mining, pp. 226–231. AAAI Press, Portland (1996)
Federal law “About strategic planning in Russian Federation" (2014). http://pravo.gov.ru/proxy/ips/?docbody=&nd=102354386
Han, X., Ma, J., Wu, Y., Cui, C.: A novel machine learning approach to rank web forum posts. Soft Comput. 18(5), 941–959 (2014)
Hubert, L., Arabie, P.: Comparing partitions. J. Classif. 2(1), 193–218 (1985). https://doi.org/10.1007/BF01908075
Jain, A.K., Murty, M.N., Flynn, P.J.: Data clustering: a review. ACM Comput. Surv. (CSUR) 31(3), 264–323 (1999)
Jolliffe, I.T.: Principal Component Analysis, p. 487. Springer, Heidelberg (1986). https://doi.org/10.1007/b98835. ISBN 978-0-387-95442-4
Li, J., Wang, K., Xu, L.: Chameleon based on clustering feature tree and its application in customer segmentation. Ann. Oper. Res. 168, 225 (2009). https://doi.org/10.1007/s10479-008-0368-4
Mansoori, E.G.: GACH: a grid based algorithm for hierarchical clustering of high-dimensional data. Soft Comput. 18(5), 905–922 (2014)
Modha, D.S., Spangler, W.S.: Feature weighting in k-means clustering. Mach. Learn. 52, 217 (2003). https://doi.org/10.1023/A:1024016609528
Mikolov T., Sutskever I., Chen K., Corrado G., Dean J.: Distributed representations of words and phrases and their compositionality. In: Proceedings of the 26th International Conference on Neural Information Processing Systems, 05–10 December, Lake Tahoe, Nevada, pp. 3111–3119 (2013)
Pedregosa, F.: Scikit-learn: machine learning in python. J. Mach. Learn. Res. 12, 2825–2830 (2011)
Le, Q., Mikolov, T.: Distributed representations of sentences and documents. In: Proceedings of the 31st International Conference on Machine Learning, PMLR, vol. 32, no. 2, pp. 1188–1196 (2014)
Yeh, R.T., Bang, S.Y.: Fuzzy relation, fuzzy graphs and their applications to clustering analysis. In: Fuzzy Sets and their Applications to Cognitive and Decision Processes, pp. 125–149. Academic Press (1975). ISBN 9780127752600
Rokach, L., Maimon, O.: Clustering methods. In: Maimon, O., Rokach, L. (eds.) Data Mining and Knowledge Discovery Handbook. Springer, Boston (2005). https://doi.org/10.1007/0-387-25465-X_15
Rosenfeld, A.: Fuzzy graphs. In: Zadeh, L.A., Fu, K.S., Tanaka, K., Shimura, M. (eds.) Fuzzy Sets and Their Applications to Cognitive and Decision Processes, pp. 77–95. Academic Press, New York (1975)
Rousseeuw, P.J.: Silhouettes: a graphical aid to the interpretation and validation of cluster analysis. Comput. Appl. Math. 20, 53–65 (1987). https://doi.org/10.1016/0377-0427(87)90125-7
Ruspini, E.H.: A new approach to clustering. Inform. Control 15(1), 22–32 (1969)
Arthur, V., et al.: K-means++: the advantages of careful seeding. In: Proceedings of the Eighteenth Annual ACM-SIAM Symposium on Discrete Algorithms. Society for Industrial and Applied Mathematics (2007)
Blondel, V.D., Guillaume, J.-L., Lambiotte, R., Lefebvre, E.: Fast unfolding of communities in large networks. J. Stat. Mech. 2008, P10008 (2008)
Zhang, J., Wang, Y., Feng, J.: A hybrid clustering algorithm based on PSO with dynamic crossover. Soft Comput. 18(5), 961–979 (2014)
Zhang, T., Ramakrishnan, R., Livny, M.: BIRCH: an efficient data clustering method for very large databases. In: Proceedings of the 1996 ACM SIGMOD International Conference on Management of Data - SIGMOD 1996, pp. 103–114 (1996). https://doi.org/10.1145/233269.233324
Acknowledgment
This study was supported Ministry of Education and Science of Russia in framework of project No 2.1182.2017/4.6 and Russian Foundation of base Research in framework of project No 16-47-732120 r_ofi_m.
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Dudarin, P., Samokhvalov, M., Yarushkina, N. (2018). An Approach to Feature Space Construction from Clustering Feature Tree. In: Kuznetsov, S., Osipov, G., Stefanuk, V. (eds) Artificial Intelligence. RCAI 2018. Communications in Computer and Information Science, vol 934. Springer, Cham. https://doi.org/10.1007/978-3-030-00617-4_17
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