Semantic-Based K-Means Clustering for Microblogs Exploiting Folksonomy
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Recently, with the development of Internet technologies and propagation of smart devices, use of microblogssuch as Facebook, Twitter, and Instagram has been rapidly increasing. Many users check for new informationon microblogs because the content on their timelines is continually updating. Therefore, clustering algorithmsare necessary to arrange the content of microblogs by grouping them for a user who wants to get the newestinformation. However, microblogs have word limits, and it has there is not enough information to analyze forcontent clustering. In this paper, we propose a semantic-based K-means clustering algorithm that not onlymeasures the similarity between the data represented as a vector space model, but also measures the semanticsimilarity between the data by exploiting the TagCluster for clustering. Through the experimental results onthe RepLab2013 Twitter dataset, we show the effectiveness of the semantic-based K-means clusteringalgorithm.
Abstract
Recently, with the development of Internet technologies and propagation of smart devices, use of microblogssuch as Facebook, Twitter, and Instagram has been rapidly increasing. Many users check for new informationon microblogs because the content on their timelines is continually updating. Therefore, clustering algorithmsare necessary to arrange the content of microblogs by grouping them for a user who wants to get the newestinformation. However, microblogs have word limits, and it has there is not enough information to analyze forcontent clustering. In this paper, we propose a semantic-based K-means clustering algorithm that not onlymeasures the similarity between the data represented as a vector space model, but also measures the semanticsimilarity between the data by exploiting the TagCluster for clustering. Through the experimental results onthe RepLab2013 Twitter dataset, we show the effectiveness of the semantic-based K-means clusteringalgorithm.
참고문헌 (7)
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