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Automatic clustering algorithm for interval data based on overlap distance.(English)Zbl 07714144

Summary: In this study, the improved overlap distance is used as a criterion in order to build clusters for interval data. This distance has shown the suitability, and given an outstanding advantage in evaluating the similarity for intervals with a lot of the considered data sets. Based on the overlap distance, we propose the Automatic Clustering Algorithm for Interval data (ACAI). One of the best advantages of the proposed algorithm is that ACAI figure out simultaneously the appropriate number of groups, and factors in every group. The proposed algorithm can be effectively performed through a Matlab procedure. Based on the extracted intervals from texture of images, we have applied ACAI to recognize the images, an interesting and challenging issue at present. Experimental data sets including the differences of the characteristics as well as the number of elements has shown the reasonableness of the proposed algorithm, and its advantages in comparing to the surviving ones. From the image recognition problem, this research has shown prospect in practical applications for many fields.

MSC:

62-XX Statistics

Software:

Matlab

Cite

References:

[1]Arumugadevi, S.; Seenivasagam, V., Color image segmentation using feedforward neural networks with FCM, International Journal of Automation and Computing, 13, 5, 491-500 (2016) ·doi:10.1007/s11633-016-0975-5
[2]Cabanes, G.; Bennani, Y.; Destenay, R.; Hardy, A., A new topological clustering algorithm for interval data, Pattern Recognition, 46, 11, 3030-9 (2013) ·doi:10.1016/j.patcog.2013.03.023
[3]Chen, C.; Quadriato, N., Clustering high dimensional categorical data via topographical features, JMLR workshop and conference proceedings (JMLR), 2732-2741 (2016)
[4]Chen, J. H.; Chang, Y. C.; Hung, W. L., A robust automatic clustering algorithm for probability density functions with application to categorizing color images, Communications in Statistics-Simulation and Computation, 47, 7, 1532-4141 (2018) ·Zbl 07550091
[5]Chen, J. C.; Hung, W. L., An automatic clustering algorithm for probability density functions, Journal of Statistical Computation and Simulation, 85, 15, 1-17 (2016)
[6]De-Carvalho, F. D.; Pimented, J. T.; Bezerra, L. T. X., Clustering of symbolic interval data based on a single adaptive L_1 distance, International Joint Conference on Neural Networks, 224-229 (2007)
[7]Deref, Q.; Fan, W. A., Information-theoretic feature selection with discrete k-median clustering, Annals of Operations Research, 263, 1, 93-118 (2018) ·Zbl 1478.62176
[8]Eleyan, A.; Demirel, H., Co-occurrence matrix and its statistical features as a new approach for face recognition, Turkish Journal of Electrical Engineering and Computer Sciences, 19, 1, 97-107 (2011)
[9]Hajjar, C.; Hamdan, H., Self-organizing map based on Hausdorff distance for interval valued data, 2011 IEEE International Conference on Systems, Man and Cybernetics, 1747-1752 (2011)
[10]Hajjar, C.; Hamdan, H., Interval data clustering using self-organizing maps based on adaptive Mahalanobis distances, Neural Networks : The Official Journal of the International Neural Network Society, 46, 3, 124-32 (2013) ·Zbl 1296.68129 ·doi:10.1016/j.neunet.2013.04.009
[11]Hubert, L.; Arabie, P., Comparing partitions, Journal of Classification, 2, 1, 193-218 (1985) ·doi:10.1007/BF01908075
[12]Hung, W.; Yang, J.; Shen, K. F., Self-updating clustering algorithm for interval-valued data, In IEEE International Conference on Fuzzy Systems:, 1494-1500 (2016)
[13]Izakian, Z.; Saadi-Mesgari, M.; Abraham, A., Automated clustering of trajectory data using a particle swarm optimization, Environment and Urban Systems, 55, 2, 55-65 (2016) ·doi:10.1016/j.compenvurbsys.2015.10.009
[14]Jeng, J. T.; Chen, C. M.; Chang, S. C.; Chuang, C. C., IPFCM clustering algorithm under Euclidean and Hausdorff distance measure for symbolic interval data, International Journal of Fuzzy Systems, 21, 7, 2102-19 (2019) ·doi:10.1007/s40815-019-00707-w
[15]Kabir, S.; Wagner, C.; Havens, T. C.; Anderson, D. T.; Aickelin, U., Novel similarity measure for interval-valued data based on overlapping ratio, IEEE International Conference on Fuzzy Systems, 16-31 (2017)
[16]Kao, C. H.; Nakano, J.; Shieh, S. H.; Tien, Y. Y.; Wu, H. M.; Yang, C. K.; Chen, C. H., Exploratory data analysis of interval-valued symbolic data with matrix visualization, Computational Statistics & Data Analysis, 79, 3, 14-29 (2014) ·Zbl 1506.62090 ·doi:10.1016/j.csda.2014.04.012
[17]Li, D. J.; Li, Y. Y.; Li, J. X.; Fu, Y., Gesture recognition based on BP neural network improved by chaotic genetic algorithm, International Journal of Automation and Computing, 15, 3, 267-76 (2018) ·doi:10.1007/s11633-017-1107-6
[18]Masson, M.-H.; Denoeux, T., Clustering interval-valued proximity data using belief functions, Pattern Recognition Letters, 25, 2, 163-71 (2004) ·doi:10.1016/j.patrec.2003.09.008
[19]Montanari, A.; Calo, D. G., Model-based clustering of probability density functions, Advances in Data Analysis and Classification, 7, 3, 301-19 (2013) ·Zbl 1273.62140 ·doi:10.1007/s11634-013-0140-8
[20]Peng, W.; Li, L., Interval data clustering with applications, 2006 IEEE international Conference on Tools with Artificial Intelligence, 355-362 (2006)
[21]Ren, Y.; Liu, Y. H.; Rong, J. R.; Dew, R., Clustering interval-valued data using an overlapped interval divergence, Proceedings of the Eighth Australasian Data Mining Conference, Vol, 101, 35-42 (2009)
[22]Sara, I. R. R.; Francisco, A. T. C., A new fuzzy clustering algorithm for interval-valued data based on City-block distance, IEEE International Conference on Fuzzy Systems, 1-6 (2019) ·doi:10.1109/FUZZ
[23]Sato-Ilic, M., Symbolic clustering with interval-valued data, Procedia Computer Science, 6, 2, 358-63 (2011) ·doi:10.1016/j.procs.2011.08.066
[24]Setia, L.; Teynor, A.; Halawani, A.; Burkhardt, H., Image classification using cluster co-occurrence matrices of local relational features, Proceedings of the 8th ACM SIGMM International Workshop on Multimedia Information Retrieval, 6, 173-182 (2006)
[25]Souza, M. C. R.; Carvalho, F. D. A., Neural Information Processing, Clustering of interval-valued data using adaptive squared Euclidean distances, 775-80 (2004)
[26]Tai, V. V., L^1-distance and classification problem by Bayesian method, Journal of Applied Statistics, 44, 3, 385-401 (2017) ·Zbl 1516.62643
[27]Tai, V. V.; Thao, N. T., Similar coefficient for cluster of probability density functions, Journal of Statistical Computation and Simulation, 47, 8, 1792-811 (2018) ·Zbl 1392.62193
[28]Tai, V. V.; Thao, N. T., Similar coefficient of cluster for discrete elements, Sankhya B, 80, 1, 19-36 (2018) ·Zbl 1395.62176
[29]Tai, V. V.; Pham-Gia, T., Clustering probability distributions, Journal of Applied Statistics, 37, 11, 1891-910 (2010) ·Zbl 1511.62142 ·doi:10.1080/02664760903186049
[30]Tai, V. V.; Ha, C. N.; Thao, N. T., Clustering for probability density functions based on genetic algorithm, 1st International Conference on Applied Mathematics in Engineering and Reliability, 51-57 (2016)
[31]Tai, V. V.; Trung, N. T.; Trung, V. D.; Vinh, H. H.; Thao, N. T., Modified genetic algorithm-based clustering for probability density functions, Journal of Statistical Computation and Simulation, 87, 10, 1964-79 (2017) ·Zbl 07192043
[32]Thao, N. T.; Tai, V. V., Fuzzy clustering of probability density functions, Journal of Applied Statistics, 44, 4, 583-601 (2017) ·Zbl 1516.62507
[33]Wang, J. S.; Ren, X. D., GLCM based extraction of flame image texture and KPCA-GLVQ recognition method for rotary combustion working conditions, International Journal of Automation and Computing, 11, 1, 72-7 (2014) ·doi:10.1007/s11633-014-0767-8
[34]Wang, K.; Long, X. X.; Li, R. F.; Zhao, L. J., A discriminative algorithm for indoor place recognition based on clustering of features and images, Journal of Applied Statistics, 14, 4, 407-19 (2017)
This reference list is based on information provided by the publisher or from digital mathematics libraries. Its items are heuristically matched to zbMATH identifiers and may contain data conversion errors. In some cases that data have been complemented/enhanced by data from zbMATH Open. This attempts to reflect the references listed in the original paper as accurately as possible without claiming completeness or a perfect matching.
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