Intelligent multimedia urban planning Construction based on spectral clustering algorithms of large data mining

  • Xing LanqinEmail author


This paper presents a spatio-temporal analysis method of intelligent urban road planning congestion based on spectral clustering algorithm of large data mining. Firstly, a time-space model of intelligent urban road planning congestion based on four-dimensional spatial temporal data of GIS is established, it uses the solution of additional virtual data to improve the sampling density of time dimension smart urban road planning congestion data. Secondly, the training planning data are clustered according to time in time and space so that the planning data with the same or similar time are in the same class. Then, each time class is clustered according to regional characteristics, and similar regions are clustered into the same block. Then it uses the Dobernoulli model to find the joint probability distribution between each block and time in the time class; finally, the joint probability distribution model is used to mine knowledge from unlabeled planning data, the effectiveness of the proposed method is verified by simulation experiments.


Big data Knowledge mining Smart city Planning and construction Spectral clustering 



Xi ‘an social science planning fund project (18 J236).


  1. 1.
    Abdulhay E, Alafeef M, Alzghoul L, Momani MA, Rabah Al Abdi AKN, Munoz R, de Albuquerque VHC (2018) Computer-aided autism diagnosis via second-order difference plot area applied to EEG empirical mode decomposition. Neural Comput & Applic.
  2. 2.
    Arunkumar N, Mohammed MA, Abd Ghani MK et al (2018) K-means clustering and neural network for object detecting and identifying abnormality of brain tumor. Soft Comput.
  3. 3.
    Arunkumar N, Mohammed MA, Mostafa SA, Ibrahim DA, Rodrigues JJPC, de Albuquerque VHC (2018) Fully automatic model-based segmentation and classification approach for MRI brain tumor using artificial neural networks. Concurrency Computat Pract Exper:e4962.
  4. 4.
    Bagyalakshmi G, Rajkumar G, Arunkumar N, Easwaran M, Narasimhan K, Elamaran V, Solarte M, Hernández I, Ramirez-Gonzalez G (2018) Network vulnerability analysis on brain signal/image databases using Nmap and Wireshark tools. IEEE Access 6:57144–57151CrossRefGoogle Scholar
  5. 5.
    Bin Z, Jia-kuan C (2003) The impact of urban planning on land use and land cover in Pudong of Shanghai,China[J]. J Environ Sci 15(2):205Google Scholar
  6. 6.
    Castro PF, Xexéo G B (2012) Granules of Words to Represent Text: An Approach Based on Fuzzy Relations and Spectral Clustering[C]// International Conference on Computational Science & Its ApplicationsGoogle Scholar
  7. 7.
    Dongdong J, Arunkumar N, Wenyu Z, Beibei L, Xinlei Z, Guangjian Z (2019) Semantic clustering fuzzy c means spectral model based comparative analysis of cardiac color ultrasound and electrocardiogram in patients with left ventricular heart failure and cardiomyopathy. Futur Gener Comput Syst 92:324–328CrossRefGoogle Scholar
  8. 8.
    Elamaran V, Arunkumar N, Hussein AF, Solarte M, Ramirez-Gonzalez G (2018) Spectral fault recovery analysis revisited with Normal and abnormal heart sound signals. IEEE Access 6:62874–62879CrossRefGoogle Scholar
  9. 9.
    Elamaran V, Arunkumar N, Babu GV, Balaji VS, Gómez J, Figueroa C, Ramirez-Gonzalez G (2018) Exploring DNS, HTTP, and ICMP response time computations on brain signal/image databases using a packet sniffer tool. IEEE Access 6:59672–59678CrossRefGoogle Scholar
  10. 10.
    Elhoseny M, Shankar K, Lakshmanaprabu SK, Andino Maseleno NA (2018) Hybrid optimization with cryptography encryption for medical image security in internet of things. Neural Comput & Applic:1–15.
  11. 11.
    Han Y, Moutarde F (2016) Analysis of large-scale traffic dynamics in an urban transportation network using non-negative tensor factorization[J]. Int J Intell Transp Syst Res 14(1):36–49Google Scholar
  12. 12.
    Haoyu L, Jianxing L, Arunkumar N, Hussein AF, Jaber MM (2018) An IoMT cloud-based real time sleep apnea detection scheme by using the SpO2 estimation supported by heart rate variability. Futur Gener Comput Syst. CrossRefGoogle Scholar
  13. 13.
    A. Hussein, N. Kumar, C. Gomes, A. AlZubaidi, Q. Habash, L. Santamaria-Granados, J. Mendoza-Moreno and G. Ramirez-Gonzalez, "Focal and non-focal epilepsy localisation: a review", IEEE Access, vol. 6, pg. 49306–49324CrossRefGoogle Scholar
  14. 14.
    Jiajie L, Narasimhan K, Elamaran V, Arunkumar N, Solarte M, Ramirez-Gonzalez G (2018) Clinical decision support system for alcoholism detection using the analysis of EEG signals. IEEE Access 6:61457–61461CrossRefGoogle Scholar
  15. 15.
    Khamparia A, Singh A, Anand D et al (2018) A novel deep learning-based multi-model ensemble method for the prediction of neuromuscular disorders. Neural Comput & Applic.
  16. 16.
    Lakshmanaprabu S.K, Sachi Mohanty, Shankar K, Arunkumar N, Gustavo Ramirez, "Optimal deep learning model for classification of lung Cancer on CT images, Futur Gener Comput Syst, Vol. 92, Mar 2019, Pg. 374–382Google Scholar
  17. 17.
    Mohammed MA, Abd Ghani MK, Arunkumar N, Hamed RI, Mostafa SA, Abdullah MK, Burhanuddin MA (2018) Decision support system for nasopharyngeal carcinoma discrimination from endoscopic images using artificial neural network. J Supercomput.
  18. 18.
    Oh SL, Hagiwara Y, Raghavendra U, Yuvaraj R, Arunkumar N, Murugappan M, Acharya UR (2018) A deep learning approach for Parkinson’s disease diagnosis from EEG signals. Neural Comput & Applic:1–7.
  19. 19.
    Peixoto SA, Filho PPR, Arun Kumar N, de Albuquerque VHC (2018) Automatic classification of pulmonary diseases using a structural co-occurrence matrix. Neural Comput & Applic:1–11.
  20. 20.
    Pereira RF, da Silva Filho VER, Moura LB, Kumar NA, de Alexandria AR, de Albuquerque VHC (2018) Automatic quantification of spheroidal graphite nodules using computer vision techniques. J Supercomput.
  21. 21.
    Petropoulos G, Partsinevelos P, Mitraka Z (2013) Change detection of surface mining activity and reclamation based on a machine learning approach of multi-temporal Landsat TM imagery[J]. Geocarto International 28(4):323–342CrossRefGoogle Scholar
  22. 22.
    U. Rajendra Achary, Yuki Hagiwara, Sunny Nitin Deshpande, S. Suren, Joel En Wei Koh, Shu Lih Oh, N. Arunkumar, Edward J. Ciaccio, Choo Min Lim., "Characterization of focal EEG signals: a review"., Futur Gener Comput Syst, Vol. 91, 2019, pg. 290–299Google Scholar
  23. 23.
    Santamaria-Granados, L, Munoz-Organero M, Ramirez-Gonzalez G, Abdulhay E, Arunkumar N (2018). Using Deep Convolutional Neural Network for Emotion Detection on a Physiological Signals Dataset (AMIGOS). IEEE Access, doi: CrossRefGoogle Scholar
  24. 24.
    Sathishkumar BR, Sundaravadivazhagan B, Martin B, Sasi G, Chandrasekar M, Kumar SR, … Arunkumar N Revisiting computer networking protocols by wireless sniffing on brain signal/image portals. Neural Comput & Applic:1–13.
  25. 25.
    Small C, Lu JWT (2006) Estimation and vicarious validation of urban vegetation abundance by spectral mixture analysis[J]. Remote Sens Environ 99(4):441–456CrossRefGoogle Scholar
  26. 26.
    Sylvain O, Pascal D, Anne P et al (2008) Optical algorithms at satellite wavelengths for Total suspended matter in tropical coastal waters[J]. Sensors 8(7):4165–4185CrossRefGoogle Scholar
  27. 27.
    Tarantino C, D'Addabbo A, Castellana L, et al. (2004) Extraction of urban settlements by an automatic approach on high resolution remote sensed data[C]// Geoscience and Remote Sensing Symposium, 2003. IGARSS '03. Proceedings. 2003 IEEE International. IEEE, 1975–1977.Google Scholar
  28. 28.
    Taşdemir K, Merényi E (2009) Exploiting data topology in visualization and clustering of self-organizing maps[J]. IEEE Trans Neural Netw 20(4):549–562CrossRefGoogle Scholar
  29. 29.
    Venkatraman V, Arunkumar N, Chantre-Astaiza A, Muñoz-Mazón AI, Fuentes-Moraleda L, Khan MS (2018) Mapping the structure and evolution of heavy vehicle research: a scientometric analysis and visualisation. Int J Heavy Vehicle Systems, Vol 25, Nos 3(/4):344–368CrossRefGoogle Scholar
  30. 30.
    Wu Z, Wang H, Arunkumar N (2019) Bayesian analysis model for the use of anesthetic analgesic drugs in cancer patients based on geometry reconstruction. Futur Gener Comput Syst 93:170–175CrossRefGoogle Scholar
  31. 31.
    Xin L, Yang D, Chen Y, et al. (2011) Traffic flow characteristic analysis at intersections from multi-layer spectral clustering of motion patterns using raw vehicle trajectory[C]// International IEEE Conference on Intelligent Transportation SystemsGoogle Scholar
  32. 32.
    Zhao J, Li R, Liang X, et al. (2015) Segmentation and evolution of urban areas in Beijing: A view from mobility data of massive individuals[C]// International Conference on Service Systems & Service ManagementGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2019

Authors and Affiliations

  1. 1.School of economics and managementShaanxi Xueqian Normal UniversityXi’anChina

Personalised recommendations