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- W2482400710 abstract "Smart city is an aspiration of the various stakeholders of the city. We strongly believe that social media can be one of the real-time data sources, which help stakeholder to realize this dream. In this paper, we have analyzed the real-time data provided by Twitter in order to empower citizens by keeping them updated about what is happening around the city. We have implemented various clustering algorithms like k-means, Hierarchical agglomerative, LDA topic modeling on Twitter stream and reported results with purity 0.476, normal mutual information (NMI) 0.3835, and F-measure 0.54. We conclude that HA-ward outperforms K-means and LDA substantially. We also conclude that results are not impressive and need to design separate feature based clustering algorithm. We have identified various tasks to mine microblog in the ambit of smart city such as event detection, geo-tagging, city clustering based upon the user activity on ground." @default.
- W2482400710 created "2016-08-23" @default.
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- W2482400710 date "2016-01-01" @default.
- W2482400710 modified "2023-10-14" @default.
- W2482400710 title "Performance Analysis of Clustering Algorithm in Sensing Microblog for Smart Cities" @default.
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- W2482400710 doi "https://doi.org/10.1007/978-981-10-0755-2_50" @default.
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