Locating the Trajectory Community for the Multi-source Scattered Modeling Based on the User Recommendation

Authors(2) :-Pooja K T, Dr. K. Thippeswamy

Data mining is an enactment of inspecting the enormous preceding databases in order to produce new information. In this paper we will be detecting communities from trajectories. In existing algorithm trajectory clustering is performed based on a single information source such as location data, regrettably additional information are ignored, due to these discovering the communities in trajectory data sets are not trustfully. To overcome these we proposed trajectory community for the multi-source scattered modelling based on the user recommendation. It combines additional information with raw trajectory data and fabricate the scattered process on multiple similitude metrics. Based on these scattered modelling we will be constructing the multi-modal scattered process and optimizing the heat kernel to learn the ordered kernel. Then compact sub-graph detection is used to discover the set of diverse communities. At last based on this information, we proposed a novel model for user recommendation.

Authors and Affiliations

Pooja K T
M.Tech in CS&E, VTU PG Centre, Mysuru, Karnataka, India
Dr. K. Thippeswamy

Trajectory, clustering, community, scattering.

[1] S. Gaffney and P. Smyth. Trajectory clustering with mixtures of regression models. In SIGKDD’99. [2] J. Huang, H. Sun, J. Han, H. Deng, Y. Sun, and Y. Liu. Shrink: a structural clustering algorithm for detecting hierarchical communities in networks. In CIKM’10. [3] J.-G. Lee, J. Han, and K.-Y. Whang. Trajectory clustering: a partition and-group framework. In SIGMOD’07. [4] J. Leskovec, K. J. Lang, and M. W. Mahoney. Empirical comparison of algorithms for network community detection. In WWW’10. [5] C. Shi, P. S. Yu, Y. Cai, Z. Yan, and B. Wu. On selection of objective functions in multi-objective community detection. In CIKM’11 [6] M. Vlachos, D. Gunopulos, and G. Kollios. Discovering similar multidimensional trajectories. In ICDE’02. [7] N. Anjum and A. Cavallaro. Multifeature object trajectory clusteringfor video analysis. IEEE TCSVT, 2008. [8] L. Backstrom and J. Leskovec. Supervised random walks: predicting andrecommending links in social networks. In WSDM’11, pages 635–644,2011. [9] M. M. Bronstein and K. Glashoff. Heat kernel coupling for multiplegraph analysis. arXiv:1312.3035v1, 2013. [10] M. M. Bronstein, K. Glashoff, and T. A. Loring. Making laplacianscommute. ArXiv:1307.6549, 2013. [11] W. Chen, W. Hsu, and M.-L. Lee. Making recommendations frommultiple domains. In KDD’13, pages 892–900, 2013. [12] M. Cuturi. Fast global alignment kernels. In ICML’11. [13] D. Eynard, K. Glashoff, M. Bronstein, and A. Bronstein. Multimodal diffusiongeometry by joint diagonalization of laplacians. ArXiv:1209.2295,2012. [14] Z. Yan, D. Chakraborty, C. Parent, S. Spaccapietra, and K. Aberer.Semantic trajectories: Mobility data computation and annotation. ACMTIST, 2012. [15] Y. Yuan, G. Wang, L. Chen, and H. Wang. Efficient subgraph similaritysearch on large probabilistic graph databases. PVLDB’12, pages 800–811, 2012. [16] B. Zheng, N. Yuan, K. Zheng, X. Xie, S. Sadiq, and X. Zhou. Approximatekeyword search in semantic trajectory database. In ICDE’15, pages 975–986, 2015.

Publication Details

Published in : Volume 4 | Issue 6 | May-June 2018
Date of Publication : 2018-05-08
License:  This work is licensed under a Creative Commons Attribution 4.0 International License.
Page(s) : 102-106
Manuscript Number : CSEIT184622
Publisher : Technoscience Academy

ISSN : 2456-3307

Cite This Article :

Pooja K T, Dr. K. Thippeswamy, "Locating the Trajectory Community for the Multi-source Scattered Modeling Based on the User Recommendation", International Journal of Scientific Research in Computer Science, Engineering and Information Technology (IJSRCSEIT), ISSN : 2456-3307, Volume 4, Issue 6, pp.102-106, May-June.2018
URL : http://ijsrcseit.com/CSEIT184622

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