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Density-based spatial clustering of applications with noise (DBSCAN) is the most commonly used density-based clustering algorithm but may not be sufficient when the input data type is <italic>heterogeneous</italic> in terms of textual description. When we aim to discover clusters of geo-tagged records relevant to a particular point of interest (POI) on social media, examining only one type of input data (e.g., the tweets relevant to a POI) may draw an incomplete picture of clusters due to noisy regions. To overcome this problem, we introduce <italic>DBSTexC</italic>, a newly defined density-based clustering algorithm using <italic>spatio-textual</italic> information on social media (e.g., Twitter). We first characterize the POI-relevant and POI-irrelevant geo-tagged tweets as the texts that include and do not include a POI name or its semantically coherent variations, respectively. By leveraging the proportion of the POI-relevant and POI-irrelevant tweets, the proposed algorithm demonstrates much higher clustering performance than the DBSCAN case in terms of <inline-formula> <tex-math notation="LaTeX">$\mathcal {F}_{1}$ </tex-math></inline-formula> score and its variants. While <italic>DBSTexC</italic> performs exactly as DBSCAN with the textually homogeneous inputs, it far outperforms DBSCAN with the textually heterogeneous inputs. Furthermore, to further improve the clustering quality by fully capturing the geographic distribution of geo-tagged points, we present <italic>fuzzy DBSTexC</italic> (<italic>F-DBSTexC</italic>), an extension of <italic>DBSTexC</italic>, which incorporates the notion of fuzzy clustering into the <italic>DBSTexC</italic>. We then demonstrate the consistent superiority of <italic>F-DBSTexC</italic> over the original <italic>DBSTexC</italic> via intensive experiments. The computational complexity of our algorithms is also analytically and numerically shown.

An Improved Density-Based Approach to Spatio-Textual Clustering on Social Media