[01] Brayan Keritino, Department of Computer Engineering, Open University of Kiev, Kiev, Ukraine. [02] Quio Feri, Department of Computer Engineering, Open University of Kiev, Kiev, Ukraine. [03] Lionip Cheler, Department of Computer Engineering, Open University of Kiev, Kiev, Ukraine.

Clustering algorithms can be categorized based on their cluster model, as listed above. The following overview will only list the most prominent examples of clustering algorithms, as there are possibly over 100 published clustering algorithms. Not all provide models for their clusters and can thus not easily be categorized. An overview of algorithms explained in Wikipedia can be found in the list of statistics algorithm. The clustering model most closely related to statistics is based on distribution models. Clusters can then easily be defined as objects belonging most likely to the same distribution. A convenient property of this approach is that this closely resembles the way artificial data sets are generated: by sampling random objects from a distribution. Here we proposed a distributed clustering to bag data clustering.

Graph Mining, Big Data Mining, Fuzzy, Distributed Dataset, Medical Networks

[01] L. Hubert, Comparing partitions. Journal of Classification, 2, 123-154 (1985). [02] A. Bewley, Real-time volume estimation of a dragline payload, IEEE International Conference on Robotics and Automation, UK, 1571-1576 (2011). [03] H. Alinejad-Rokny, A Method to AvoidGapped Sequential Patterns in Biological Sequences: Case Study: HIV and Cancer Sequences, Journal of Neuroscience and Neuroengineering, 4(1), (2015). [04] Z. Deng, and Z. Wang, A New Algorithm for Fast Mining Frequent Itemsets Using N-Lists, Science China Information Sciences, 55, 2008-2030 (2012). [05] Z. Deng, and Z. Wang, A New Fast Vertical Method for Mining Frequent Patterns, International Journal of Computational Intelligence Systems, 3, 733-744 (2010). [06] G. Webb, OPUS: An Efficient Admissible Algorithm for Unordered Search, Journal of Artificial Intelligence Research, 4, 531-565 (1995). [07] Hasanzadeh E., Poyan M., Alinejad-Rokny H. (2012). “Text Clustering on Latent Semantic Indexing with Particle Swarm Optimization (PSO) Algorithm”, International Journal of the Physical Sciences, 7(1): 116-120. [08] D. Bayardo, Constraint-based rule mining in large, dense databases, Data Mining and Knowledge Discovery, 4, 217-240 (2013). [09] H. Alinejad-Rokny, Proposing on Optimized Homolographic Motif Mining Strategy based on Parallel Computing for Complex Biological Networks, Journal of Medical Imaging and Health Informatics, 7, (2015). [10] F. Salleb-Aouissi, QuantMiner: A Genetic Algorithm for Mining Quantitative Association Rules, International Joint Conference on Artificial Intelligence, Paris, 1035-1040 (2011). [11] Esmaeili L., Minaei-Bidgoli B., Alinejad-Rokny H. (2012). “A Hybrid Recommender System for Joining Virtual Communities”, Research Journal of Applied Sciences Engineering and Technology, 4(5): 500-509. [12] S. Azevedo, and Santos, a parallel overview, In Proceedings of the IADIS European Conference on Data Mining, UK, 182-185 (2008). [13] A. Braha, and T. Elovici, Theory of actionable data mining with application to semiconductor manufacturing control, International Journal of Production Research, 47, 234-254 (2009). [14] D. Braha, and A. Shmilovici, On the Use of Decision Tree Induction for Discovery of Interactions in a Photolithographic Process, IEEE Transactions on Semiconductor Manufacturing, 16, 456-476 (2006). [15] H.A. Rowley, S. Baluja, and T. Kanade, Neural network-based face detection, IEEE Transactions on Pattern Analysis and Machine Intelligence, 20, 29-38 (1998). [16] Parvin H., Alinejad-Rokny H., Parvin S. (2013). A Classifier Ensemble of Binary Classifier Ensembles, International Journal of Learning Management Systems, 1(2): 37-47. [17] H. Alinejad-Rokny, H. Pourshaban, A. Goran, M. MirnabiBaboli, “Network Motifs Detection Strategies and Using for Bioinformatic Networks”. Journal of Bionanoscience 8(5), 353-359 (2014). [18] S.Z. Li, L. Zhu, Z.Q. Zhang, A. Blake, H.J. Zhang, and H. Shum, Statistical learning of multi-view face detection, European Conference on Computer Vision, Paris, 67-81 (2002). [19] Motameni H., Alinejad-Rokny H., Pedram M.M. (2011). “Using Sequential Pattern Mining in Discovery DNA Sequences Contain Gap”, American Journal of Scientific Research, 14, 72-78. [20] Alinejad-Rokny H., Pourshaban H., Goran A., MirnabiBaboli M. (2014). “Network Motifs Detection Strategies and Using for Bioinformatic Networks”, Journal of Bionanoscience, 8(5): 353-359. [21] Alinejad Rokny H., Pedram M.M., Shirgahi H. (2011). “Discovered motifs with using parallel Mprefixspan method”, Scientific Research and Essays, 6(20): 4220-4226. [22] X.R. Chen, and A. Yuille, Detecting and reading text in natural scenes, IEEE International Conference on Computer Vision and Pattern Recognition, US, 366-373 (2004). [23] MirnabiBaboli M., RashidiBajgan S., Alinejad-Rokny H. (2015). “The Enhancement of Network Lifetime by Mobile Node in OPP Routing of Wireless Sensor Network”, Information Sciences Letters, 4(1): 1-6. [24] K.I. Kim, K. Jung, and J.H. Kim, Texture-based approach for text detection in images using support vector machines and continuously adaptive mean shift algorithm, IEEE Transactions on Pattern Analysis and Machine Intelligence, 25(12), 1631-1639 (2003). [25] H. Alinejad Rokny, M.M. Pedram, H. Shirgahi, Discovered motifs with using parallel Mprefixspan method, Scientific Research and Essays, 6(20), 4220-4226 (2011). [26] M. Hadid, K. Pietika, and T. Ahonen, A discriminative feature space for detecting and recognizing faces, Conference on Computer Vision and Pattern Recognition UK, 12(3). 797-804 (2004).