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Erschienen in:
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2016 | OriginalPaper | Buchkapitel

Analysis of Mining, Visual Analytics Tools and Techniques in Space and Time

verfasst von : K. Nandhini, I. Elizabeth Shanthi

Erschienen in: Proceedings of the Second International Conference on Computer and Communication Technologies

Verlag: Springer India

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Abstract

All living things are connected to the space and time, which really shows a necessity to improve their sophistication for leading a better life. Exploration and prediction in space and time has been the tough chore to the researchers and developers. Development in the technology helps to elevate the persevering difficulties. Two interdisciplinary approaches in the computer science that has become pre-eminent in the effective analysis of space and time are data mining and visual analytics. Visual analytics is one interactive user interface where we can explore and visualize the data using visual analytic tools. So, visual analytics with the complex data requires a competent approach for accuracy which is nevertheless a data mining process. But the real scenario is, techniques and tools are more developed but may not nail in terms of accuracy and speed for handling complex-and time-oriented data. The main cause of the dearth may be more new tools and techniques developed by more researchers are not deliberated. The mission of the research paper is to study the techniques and tools of data mining and visual analytic in space and time.

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Vorheriges Kapitel Intelligent Traffic Monitoring System
Nächstes Kapitel Dimensionality Reduced Recursive Filter Features for Hyperspectral Image Classification
Literatur
1.
Han, J., Kamber, M.: Data Mining: Concepts and Techniques. Morgan Kauffman, San Francisco (2006)
2.
Mohammed, A., et al.: A unified approach for spatial data query. Int. J. Data Mining Knowl. Manag. Process 3(6), 55–71 (2013)CrossRef
3.
Sun, G.D., Wu, Y.C., Liang, R.H., et al.: A survey of visual analytics techniques and applications: state-of-the-art research and future challenges. J. Comput. Sci. Technol. 28(5), 852–867 (2013)CrossRef
4.
Fotheringham, S.A., Rogerson, P.A.: The SAGE Handbook of Spatial Analysis. SAGE Publications, London (2008)
5.
6.
Gennady, A., et al.: Space, time and visual analytics. Int. J. Geogr. Inf. Sci. 24(10), 1577–1600 (2010)CrossRef
7.
Angela, L., Schmidt, A., Tischendorf, L.: Data mining and linked open data–new perspectives for data analysis in environmental research. Ecol. Model. 295, 5–17 (2015)CrossRef
8.
Yoo, J.S., Boulware, D., Kimmey, D.: Incremental and parallel association mining for evolving spatial data: a less iterative approach on map reduce (2015)
9.
Jeremy, M., Guo, D.: Spatial data mining and geographic knowledge discovery—an introduction. Comput. Environ. Urban Syst. 33(6), 403–408 (2009)CrossRef
10.
Yuzuru, Tanaka, et al.: Geospatial visual analytics of traffic and weather data for better winter road management. data mining for geoinformatics, pp. 105–126. Springer, New York (2014)
11.
Petelin, B., et al.: Multi-level association rules and directed graphs for spatial data analysis. Expert Syst. Appl. 40(12), 4957–4970 (2013)CrossRef
12.
Xie, Y., et.al.: Silverback: Scalable association mining for temporal data in columnar probabilistic databases. Data Engineering (ICDE), IEEE, pp. 1072–1083 (2014)
13.
Wei, Tian, et al.: A survey on clustering based meteorological data mining. Int. J. Grid Distrib. Comput. 7(6), 229–240 (2014)
14.
Antunes, C.M., Oliveira, A.L.: Temporal data mining: an overview. KDD workshop on temporal data mining, pp. 1–13 (2001)
15.
16.
Perer, A., Sun, J.: MatrixFlow: temporal network visual analytics to track symptom evolution during disease progression. In: AMIA Annual Symposium Proceedings, vol. 2012, pp. 716–725 (2012)
17.
18.
Xue, C. J., Q. Dong., W. X. Ma.: Object-oriented spatial-temporal association rules mining on ocean remote sensing imagery. In: IOP Conference Series: Earth and Environmental Science, vol. 17, no. 1, IOP Publishing, (2014)
19.
Freddy, L., et al.: Smart traffic analytics in the semantic web with STAR-CITY: scenarios, system and lessons learned in Dublin City. Web Semantics: Science, Services and Agents on the World Wide Web 27, 26–33 (2014)
20.
Ma, W., Xue, C., Zhou, J.: Mining time-series association rules from Western Pacific spatial-temporal data. In: IOP Conference Series: Earth and Environmental Science. vol. 17. no. 1. IOP Publishing (2014)
21.
Musdholifah, A., Hashim, S.Z.M.: Triangular kernel nearest neighbor based clustering for pattern extraction in spatio-temporal database. In: Intelligent Systems Design and Applications (ISDA), pp. 67–73. IEEE (2010)
22.
Musdholifah, A., Hashim, S.Z.M.: Scatter-PCA for visual clustering of spatio-temporal data. IJCSNS 14(1), 72–76 (2014)
23.
Munson, Michael E., et al.: Data mining for identifying novel associations and temporal relationships with Charcot foot. Journal of diabetes research, Vol. 2014 (2014)
24.
Gudmundsson, J., Wolle, T.: Football analysis using spatio-temporal tools. Comput. Environ. Urban Syst. 47, 16–27 (2014)CrossRef
25.
Reddy, P.: Sequential spatio-temporal pattern mining with time lag. Dissertation. University of Illinois, Chicago (2014)
26.
Tayyab Asif, M., et al.: Spatiotemporal patterns in large-scale traffic speed prediction. IEEE Trans. Intell. Transp. Syst. 15(2), 794–804 (2014)CrossRef
27.
Mohan, A.: A new spatio-temporal data mining method and its application to reservoir system operation. Dissertation. University of Nebraska, Nebraska (2014)
28.
Schubert, E., Zimek, A., Kriegel, H.P.: Generalized outlier detection with flexible kernel density estimates. In: Proceedings of the 14th SIAM International Conference on Data Mining (SDM), Philadelphia (2014)
29.
Viswanath, B., et al. : Towards detecting anomalous user behavior in online social networks. In: Proceedings of the 23rd USENIX Security Symposium (USENIX Security), pp. 223–238. San Deigo (2014)
30.
Lu, T., Wu, L., Ma, X., Shivakumara, P., Tan, C.L.: Anomaly detection through spatio-temporal context modeling in crowded scenes. In: 22nd International Conference Pattern Recognition (ICPR), pp. 2203–2208. IEEE (2014)
31.
Olislagers, F., Worring, M.: The spatiotemporal multivariate hypercube for discovery of patterns in event data. In: IEEE Conference Visual Analytics Science and Technology (VAST), pp. 235–236. IEEE (2012)
32.
Junghoon, C., et al.: Public behavior response analysis in disaster events utilizing visual analytics of microblog data. Comput. Graphics 38, 51–60 (2014)CrossRef
33.
Markus, H., et al.: Uncertainty-aware video visual analytics of tracked moving objects. J. Spat. Inform. Sci. 2, 87–117 (2015)
34.
Straumann, R.K., Çöltekin, A., Andrienko, G.: Towards (Re) constructing narratives from georeferenced photographs through visual analytics. Cartogr. J. 51(2), 152–165 (2014)CrossRef
35.
Zhu, X., Guo, D.: Mapping large spatial flow data with hierarchical clustering. Trans. GIS 18(3), 421–435 (2014)MathSciNetCrossRef
36.
Leavitt, N.: Will NoSQL databases live up to their promise? Computer 43(2), 12–14 (2010)CrossRef
37.
38.
39.
40.
Metadaten
Titel
Analysis of Mining, Visual Analytics Tools and Techniques in Space and Time
verfasst von
K. Nandhini
I. Elizabeth Shanthi
Copyright-Jahr
2016
Verlag
Springer India
Buch
Proceedings of the Second International Conference on Computer and Communication Technologies

Print ISBN: 978-81-322-2522-5

Electronic ISBN: 978-81-322-2523-2

Copyright-Jahr: 2016

DOI
https://doi.org/10.1007/978-81-322-2523-2_53

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