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. Bibliographie,

H. Abdi and L. Williams, Principal Component Analysis, pp.433-459
URL : https://hal.archives-ouvertes.fr/hal-01259094

M. Ankerst, OPTICS : ordering points to identify the clustering structure, ACM Sigmod record. T. 28. 2. ACM, pp.49-60, 1999.

D. Arthur and S. Vassilvitskii, k-means++ : The advantages of careful seeding, Proceedings of the eighteenth annual ACM-SIAM symposium on Discrete algorithms, pp.1027-1035, 2007.

J. L. Bentley, Multidimensional binary search trees used for associative searching, Communications of the ACM, vol.18, pp.509-517, 1975.

J. A. Bondy, Graph Theory With Applications, p.444194517, 1976.

H. Bunke, Graph clustering using the weighted minimum common supergraph, GbRPR 2726, pp.235-246, 2003.

J. Ricardo, D. Campello, J. Moulavi, and . Sander, Density-based clustering based on hierarchical density estimates, Pacific-Asia Conference on Knowledge Discovery and Data Mining, pp.160-172, 2013.

D. Chang, A dynamic niching clustering algorithm based on individualconnectedness and its application to color image segmentation, Pattern Recognition, vol.60, pp.31-3203, 2016.

Y. Cheng, Mean shift, mode seeking, and clustering, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.17, pp.790-799, 1995.

. Charles-k-chui, Multivariate splines. SIAM, 1988.

P. Arthur, N. M. Dempster, . Laird, and . Donald-b-rubin, Maximum likelihood from incomplete data via the EM algorithm, Journal of the royal statistical society. Series B (methodological, pp.1-38, 1977.

B. Desgraupes, clusterCrit : Clustering indices, pp.4-5

H. Du, S. Zhao, and D. Zhang, Robust Local Outlier Detection, 2015 IEEE International Conference on Data Mining Workshop (ICDMW), pp.116-123, 2015.

J. C. Dunn, A Fuzzy Relative of the ISODATA Process and Its Use in Detecting Compact Well-Separated Clusters, Journal of Cybernetics, vol.3, pp.32-57, 1973.

M. Ester, A density-based algorithm for discovering clusters in large spatial databases with noise, vol.34, pp.226-231, 1996.

. Thomas-s-ferguson, A Bayesian analysis of some nonparametric problems, The annals of statistics, pp.209-230, 1973.

P. Fränti, Clustering datasets, 2015.

L. Gomes, 3rd International Conference on Information Technology and Quantitative Management, ITQM 2015 Image Segmentation via Improving Clustering Algorithms with Density and Distance, vol.55, pp.1877-0509, 2015.

R. Gray, Vector quantization, IEEE Assp Magazine, vol.1, issue.2, pp.4-29, 1984.

M. A. Hearst, Support vector machines, IEEE Intelligent Systems and their applications, vol.13, pp.18-28, 1998.

S. Impedovo, More than twenty years of advancements on Frontiers in handwriting recognition, Pattern Recognition, vol.47, pp.916-928, 2014.

S. Jia, G. Tang, and J. Hu-;-de-xiaofei-he, Band Selection of Hyperspectral Imagery Using a Weighted Fast Density Peak-Based Clustering Approach, Intelligence Science and Big Data Engineering. Image and Video Data Engineering : 5th International Conference, pp.50-59, 2015.

X. Han, ;. Clustering-;-de, C. Sammut, G. I. Webb, and M. A. Boston, Encyclopedia of Machine Learning. Sous la dir, pp.564-565, 2010.

. Stephen-c-johnson, Hierarchical clustering schemes, Psychometrika, vol.32, pp.241-254, 1967.

S. Kaski, J. Sinkkonen, and A. Klami, Discriminative clustering, Neurocomputing, vol.69, pp.18-41, 2005.

L. Kaufman and P. Rousseeuw, Clustering by means of medoids, 1987.

T. Kohonen, Self-organized formation of topologically correct feature maps, Biological cybernetics, vol.43, pp.59-69, 1982.

B. Korte and J. Vygen, Combinatorial Optimization : Theory and Algorithms. 4th, p.9783540718437, 2007.

, La régression linéaire simple, Régression : Théorie et applications, pp.1-32, 2007.

S. Li, An efficient clustering method for medical data applications, Cyber Technology in Automation, Control, and Intelligent Systems (CYBER), 2015 IEEE International Conference on, pp.133-138, 2015.

S. Li, Concise formulas for the area and volume of a hyperspherical cap, Asian Journal of Mathematics and Statistics, vol.4, pp.66-70, 2011.

Z. Liang and P. Chen, Delta-density based clustering with a divide-andconquer strategy : 3DC clustering, Pattern Recognition Letters, vol.73, pp.167-8655, 2016.

D. Liu, S. F. Cheng, and Y. Yang, Density Peaks Clustering Approach for Discovering Demand Hot Spots in City-scale Taxi Fleet Dataset, 2015 IEEE 18th International Conference on Intelligent Transportation Systems, pp.1831-1836, 2015.

P. Liu, A Text Clustering Algorithm Based on Find of Density Peaks, 2015 7th International Conference on Information Technology in Medicine and Education (ITME), pp.348-352, 2015.

C. Ma, T. Ma, and H. Shan, A new important-place identification method, Computer and Communications (ICCC), 2015 IEEE International Conference on, pp.151-155, 2015.

J. Macqueen, Some methods for classification and analysis of multivariate observations, Proceedings of the Fifth Berkeley Symposium on Mathematical Statistics and Probability, vol.1, pp.281-297, 1967.

G. Mclachlan and T. Krishnan, The EM algorithm and extensions. T. 382, 2007.

R. Mehmood, Clustering by fast search and find of density peaks via heat diffusion, Neurocomputing, pp.925-2312, 2016.

R. Mehmood, Fuzzy Clustering by Fast Search and Find of Density Peaks, 2015 International Conference on Identification, Information, and Knowledge in the Internet of Things (IIKI), pp.258-261, 2015.

L. Miao, Comparative Analysis of Two Clustering Algorithms : K-means and FSDP (Fast Search and Find of Density Peaks, Master's Project

. Mém and . Mast, , 2015.

A. Papoulis, Signal analysis. McGraw-Hill electrical and electronic engineering series, 1977.

F. Pedregosa, Scikit-learn : Machine Learning in Python, Journal of Machine Learning Research, vol.12, pp.2825-2830, 2011.
URL : https://hal.archives-ouvertes.fr/hal-00650905

C. Dzung-l-pham, . Xu, L. Jerry, and . Prince, Current methods in medical image segmentation, Annual review of biomedical engineering, vol.2, issue.1, pp.315-337, 2000.

Y. Qin, C. Zhao, and F. Gao, An iterative two-step sequential phase partition (ITSPP) method for batch process modeling and online monitoring, AIChE Journal, vol.62, pp.1547-5905, 2016.

J. O. Ramsay, Functional Data Analysis, Encyclopedia of Statistical Sciences, 2004.

C. Edward-rasmussen, The infinite Gaussian mixture model, Advances in neural information processing systems, pp.554-560, 2000.

A. Robles-kelly, . Edwin, and . Hancock, Graph edit distance from spectral seriation, IEEE transactions on pattern analysis and machine intelligence, vol.27, pp.365-378, 2005.

A. Rodriguez and A. Laio, Clustering by fast search and find of density peaks, Science, vol.344, pp.1492-1496, 2014.

H. Sak, Fast and accurate recurrent neural network acoustic models for speech recognition, 2015.

W. David and . Scott, Multivariate density estimation : theory, practice, and visualization, 2015.

M. Shevtsov, A. Soupikov, and A. Kapustin, Highly Parallel Fast KD-tree Construction for Interactive Ray Tracing of Dynamic Scenes, Computer Graphics Forum. T. 26. 3. Wiley Online Library, pp.395-404, 2007.

Y. Shi, A novel clustering-based image segmentation via density peaks algorithm with mid-level feature, Neural Computing and Applications, pp.1433-3058, 2016.

. Bernard-w-silverman, Density estimation for statistics and data analysis. T. 26, 1986.

M. Song and D. Civco, Road extraction using SVM and image segmentation, Photogrammetric Engineering & Remote Sensing, vol.70, pp.1365-1371, 2004.
DOI : 10.14358/pers.70.12.1365

. Robert-endre-tarjan, Data structures and network algorithms. SIAM, 1983.

N. Tishby, C. Fernando, W. Pereira, and . Bialek, The information bottleneck method, 2000.

G. Wang and Q. Song, Automatic Clustering via Outward Statistical Testing on Density Metrics, IEEE Transactions on Knowledge and Data Engineering, vol.28, pp.1041-4347, 2016.
DOI : 10.1109/tkde.2016.2535209

S. Wang, Clustering by Fast Search and Find of Density Peaks with Data Field, Chinese Journal of Electronics, vol.25, pp.397-402, 2016.
DOI : 10.1049/cje.2016.05.001

X. Wang and Y. Xu, Fast clustering using adaptive density peak detection, Statistical Methods in Medical Research, 2015.
DOI : 10.1177/0962280215609948

Y. Wang, Clustering of Electricity Consumption Behavior Dynamics toward Big Data Applications, IEEE Transactions on Smart Grid PP, vol.99, pp.1949-3053, 2016.
DOI : 10.1109/tsg.2016.2548565

H. John and . Wolfe, Pattern clustering by multivariate mixture analysis, Multivariate Behavioral Research, vol.5, pp.329-350, 1970.

J. Xie, Robust clustering by detecting density peaks and assigning points based on fuzzy weighted K-nearest neighbors, Information Sciences, vol.354, pp.19-40, 2016.
DOI : 10.1016/j.ins.2016.03.011

L. Xu, Maximum margin clustering, Advances in neural information processing systems, pp.1537-1544, 2005.

J. Yu, A density peak clustering approach to unsupervised acoustic subword units discovery, 2015 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference (APSIPA), pp.178-183, 2015.
DOI : 10.1109/apsipa.2015.7415498

. Charles-t-zahn, Graph-theoretical methods for detecting and describing gestalt clusters, IEEE Transactions on computers, vol.100, pp.68-86, 1971.

R. Zhang, An Improved Fast Search Clustering Algorithm Based on Kernel Density, 2015 IEEE International Conference on Smart City/SocialCom/SustainCom (SmartCity), pp.689-693, 2015.
DOI : 10.1109/smartcity.2015.149

W. Zhao, Face recognition : A literature survey, ACM computing surveys (CSUR), vol.35, pp.399-458, 2003.