Abstract
The growing popularity of micro-blogging phenomena opens up a flexible platform for the public as communication media for the public. For any trending/non-trending topic, thousands of post are posted daily in micro-blogs. During any important event, such as natural calamity and election, and sports event, such as IPL and World Cup, a huge number of messages (micro-blogs) are posted. Due to fast and huge exchange of messages causes information overload, hence clustering or grouping similar messages is an effective way to reduce that. Less content and noisy nature of messages are challenging factor in micro-blog data clustering. Incremental huge data is another challenge to clustering. So, in this work, a novel clustering approach is proposed for micro-blogs combining feature selection technique. The proposed approach has been applied to several experimental dataset, and it is compared with several existing clustering techniques which results in better outcome than other methods.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Blei, D.M., Ng, A.Y., Jordan, M.I.: Latent dirichlet allocation. J. Mach. Learn. Res. 3, 993–1022 (2003). URL http://dl.acm.org/citation.cfm?id=944919.944937
Cheong, M., Lee, V.C.S.: A study on detecting patterns in twitter intra-topic user and message clustering. In: ICPR, pp. 3125–3128. IEEE Computer Society (2010). URL http://dblp.uni-trier.de/db/conf/icpr/icpr2010.html#CheongL10
Dueck, D.: Affinity propagation: clustering data by passing messages. Ph.D. thesis, Citeseer (2009)
Dutta, S., Ghatak, S., Roy, M., Ghosh, S., Das, A.K.: A graph based clustering technique for tweet summarization. In: 2015 4th International Conference on Reliability, Infocom Technologies and Optimization (ICRITO) (Trends and Future Directions), pp. 1–6 (2015). DOI https://doi.org/10.1109/ICRITO.2015.7359276
Typhoon Hagupit – Wikipedia (2014). http://en.wikipedia.org/wiki/Typhoon_Hagupit
Hill, S., Benton, A., Ungar, L., Macskassy, S., Chung, A., Holmes, J.H.: A cluster-based method for isolating influence on twitter (2016)
Hyderabad blasts – Wikipedia (2013). http://en.wikipedia.org/wiki/2013_Hyderabad_blasts
Karypis, G., Han, E.H.S., Kumar, V.: Chameleon: Hierarchical clustering using dynamic modeling. Computer 32(8), 68–75 (1999). DOI https://doi.org/10.1109/2.781637. URL http://dx.doi.org/10.1109/2.781637
Liu, Y., Li, Z., Xiong, H., Gao, X., Wu, J.: Understanding of internal clustering validation measures. In: Proceedings of the 2010 IEEE International Conference on Data Mining, ICDM ’10, pp. 911–916. IEEE Computer Society, Washington, DC, USA (2010). DOI https://doi.org/10.1109/ICDM.2010.35. URL http://dx.doi.org/10.1109/ICDM.2010.35
Rangrej, A., Kulkarni, S., Tendulkar, A.V.: Comparative study of clustering techniques for short text documents. In: Proceedings of the 20th international conference companion on World wide web, pp. 111–112. ACM (2011)
Sandy Hook Elementary School shooting – Wikipedia (2012). http://en.wikipedia.org/wiki/Sandy_Hook_Elementary_School_shooting
Sontag, D., Roy, D.: Complexity of inference in latent dirichlet allocation. In: J. Shawe-Taylor, R.S. Zemel, P.L. Bartlett, F. Pereira, K.Q. Weinberger (eds.) Advances in Neural Information Processing Systems 24, pp. 1008–1016. Curran Associates, Inc. (2011). URL http://papers.nips.cc/paper/4232-complexity-of-inference-in-latent-dirichlet-allocation.pdf
Thomas, A.M., Resmipriya, M.: An efficient text classification scheme using clustering. Procedia Technology 24, 1220–1225 (2016)
REST API Resources, Twitter Developers. https://dev.twitter.com/docs/api
North India floods – Wikipedia (2013). http://en.wikipedia.org/wiki/2013_North_India_floods
Yang, J., Leskovec, J.: Patterns of temporal variation in online media. In: Proceedings of the fourth ACM international conference on Web search and data mining, pp. 177–186. ACM (2011)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2019 Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Dutta, S., Ghatak, S., Das, A.K., Gupta, M., Dasgupta, S. (2019). Feature Selection-Based Clustering on Micro-blogging Data. In: Behera, H., Nayak, J., Naik, B., Abraham, A. (eds) Computational Intelligence in Data Mining. Advances in Intelligent Systems and Computing, vol 711. Springer, Singapore. https://doi.org/10.1007/978-981-10-8055-5_78
Download citation
DOI: https://doi.org/10.1007/978-981-10-8055-5_78
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-10-8054-8
Online ISBN: 978-981-10-8055-5
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)