Combination of singular value decomposition and K-means clustering methods for topic detection on Twitter

Online social media are growing very rapidly in recent years, such as Twitter. Even the interaction and communication in the social media can reflect on the events of the real world. This causes the value of the information increasing significantly. However, the huge amount of the information requires a method of automatically detecting topics, one of which is the K-means Clustering. Moreover, the large dimensions of data become obstacles. So, we used singular value decomposition (SVD) to reduce the dimension of the data prior to the learning process using the K-means Clustering. The accuracy of the combination of SVD and K-means Clustering methods showed comparative results, while the computation time required is likely to be faster than the method of K-means Clustering without any reduction in advance.