Abstract
For attaining spatial time-series data in past one decade many of the attempts have been implemented on data sets to perform various processes for mining and classifying prediction rules. A novel approach is proposed in this paper for mining time-series data on cloud model we used wallmart data set. This process is performed over numerical characteristic oriented datasets. The process includes theory of cloud model with expectation, entropy and hyper-entropy characteristics. Then data is attained using backward cloud model by implementing on Libvirt. Using curve fitting process numerical characteristics are predicted. The proposed model is considerably feasible and is applicable in performing forecasting over cloud.
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References
Li DR, Wang SL, Shi WZ et al (2001) On spatial data mining and knowledge discovery. GeomatS Inf Sci Wuhan Univ 26(6):491–499
Shekhar S, Zhang P, Huang Y et al (2003) Trends in spatial data mining. In: Kargupta H, Joshi A (eds) Data mining: next generation challenges and future directions. AAAI/MIT Press, Menlo Park, pp 357–380
Ji X et al (2015) PRACTISE: robust prediction of data center time series. In: International conference on network & service management
Box G, Jenkins GM (1976) Time series analysis: forecasting and control. Holden Day Inc., San Francisco
Han J, Dong G, Yin Y (1999) Efficient mining of partial periodic patterns in time series database. In: Proceedings of 1999 international conference on data engineering (ICDE’99), pp 106–115. Sydney, Australia, April 1999
Ozden B, Ramaswamy S, Silberschatz A (1998) Cyclic association rules. In: Proceedings of the 15th international conference on data engineering, pp 412–421
Li Y, Ning P, Wang XS et al (2003) Discovering calendar-based temporal association rules. Data & Knowl Eng 44:193–218
Agrawal R, Lin KI, Sawhney HS et al (1995) Fast similarity search in the presence of noise, scaling, and translation in time-series databases. In: Proceedings of the 21th international conference on very large data bases, pp 490–501
Pavlidis T, Horowitz SL (1974) Segmentation of plane curves. IEEE Trans Comput 23:860–870
Park S, Kim SW, Chu WW (2001) Segment-based approach for subsequence searches in sequence databases. In: Proceedings of the sixteenth ACM symposium on applied computing, pp 248–252
Pratt KB, Fink E (2002) Search for patterns in compressed time series. Int J Image Graph 2(1):89–106
Xiao H, Hu YF (2005) Data mining based on segmented time warping distance in time series database. Comput Res Dev 42(1):72–78
Cui WH, Guan ZQ, Qin KA (2008) Multi-scale image segmentation algorithm based on the cloud model. In: Proceedings of the 8th spatial accuracy assessment in natural resources, World Academic Union
Tang XY, Chen KY, Liu YF (2009) Land use classification and evaluation of RS image based on cloud model. In: Liu Y, Tang X (eds) Proceedings of the SPIE, vol 7492, 74920N-74920N-8
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Narasimha Rao, S., Ram Kumar, P. (2020). Time Series Data Mining in Cloud Model. In: Satapathy, S.C., Raju, K.S., Shyamala, K., Krishna, D.R., Favorskaya, M.N. (eds) Advances in Decision Sciences, Image Processing, Security and Computer Vision. ICETE 2019. Learning and Analytics in Intelligent Systems, vol 3. Springer, Cham. https://doi.org/10.1007/978-3-030-24322-7_31
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DOI: https://doi.org/10.1007/978-3-030-24322-7_31
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