2019 VOL.2 Feb No.1

• Title: Fine-grained data based on the associated large multidimensional data mining algorithms
• Name: Carina Taylor
• Company: University of North Carolina,The United States of America
• Abstract:

   Large data mining algorithms prevalent for a long time, low efficiency, accuracy and low defects. Thus, the proposed algorithm based on mining large multidimensional data associated with fine-grained data, large data information is read into memory, then the associated multidimensional data structure of fine-grained clustering, data mining operations eventually large. The superiority of multi-dimensional association fine-grained data mining algorithm based on big data is verified by simulation experiments. Compared with the conventional methods, found that the accuracy of the data mining process large data processing 10.62% higher than the traditional methods, 13.47% higher efficiency than conventional methods.

• Keyword: big data; multi-dimensional association fine-grained data; mining calculation;
• DOI: 10.12250/jpciams2019010120
• Citation form: Carina Taylor.Fine-grained data based on the associated large multidimensional data mining algorithms[J]. Computer Informatization and Mechanical System, 2019, vol. 2, pp. 46-50.

[1] HuZhongda, YangChangchun, YiLeiyang. Simulation design of ground motion accelerometer based on non-restrictive structure [J]. Computer Simulation, 2016, 33(1):127-132.
[2] Park H, Cox D T, Barbosa A R. Comparison of inundation depth and momentum flux based fragilities for probabilistic tsunami damage assessment and uncertainty analysis[J]. Coastal Engineering, 2017, 122:10-26.
[3] Bai Y, Adriano B, Mas E, et al. Object-Based Building Damage Assessment Methodology Using Only Post Event ALOS-2/PALSAR-2 Dual Polarimetric SAR Intensity Images (Special Issue on Disaster and Big Data(Part 2))[J]. Journal of Disaster Research, 2017, 12(2):259-271.
[4] Lallemant D, Soden R, Rubinyi S, et al. Post-disaster damage assessments as catalysts for recovery: A look at assessments conducted in the wake of the 2015 earthquake in Nepal[J]. Earthquake Spectra, 2017, 33(S1).
[5] Espinoza S, Panteli M, Mancarella P, et al. Multi-phase assessment and adaptation of power systems resilience to natural hazards[J]. Electric Power Systems Research, 2016, 136:352-361.
[6] Kim J H, Chau-Dinh T, Zi G, et al. Probabilistic fatigue integrity assessment in multiple crack growth analysis associated with equivalent initial flaw and material variability[J]. Engineering Fracture Mechanics, 2016, 156:182-196.
[7] Rahman M U, Rahman S, Mansoor S, et al. Implementation of ICT and Wireless Sensor Networks for Earthquake Alert and Disaster Management in Earthquake Prone Areas [J]. Procedia Computer Science, 2016, 85:92-99.
[8] Oguchi M, Hara R. A Speculative Control Mechanism of Cloud Computing Systems Based on Emergency Disaster Information Using SDN [J]. Procedia Computer Science, 2016, 98:515-521.
[9] Min G E, Chen X, Fengping W U. Disaster Chain,Loss of Victims in the Disaster and Multi-period Allocation of Complex Emergency Resources Networks[J]. Journal of Beijing Institute of Technology, 2017.
[10] Festa G, Picozzi M, Caruso A, et al. Performance of Earthquake Early Warning Systems during the 2016–2017 Mw 5–6.5 Central Italy Sequence[J]. Seismological Research Let.