DBSTexC Density-Based Spatio-Textual Clustering on Twitter

Density-based spatial clustering of applications with noise (DBSCAN) is the most commonly used density-based clustering algorithm, where it can discover multiple clusters with arbitrary shapes. DBSCAN works properly when the input data type is homogeneous, but the DBSCAN's approach may not be s...

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Veröffentlicht in:Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017 S. 23 - 26
Hauptverfasser: Nguyen, Minh D., Shin, Won-Yong
Format: Tagungsbericht
Sprache:Englisch
Veröffentlicht: New York, NY, USA ACM 31.07.2017
Schriftenreihe:ACM Conferences
Schlagworte:
Computing methodologies
Computing methodologies > Machine learning
Computing methodologies > Machine learning > Learning paradigms
Computing methodologies > Machine learning > Learning paradigms > Unsupervised learning
Human-centered computing
Human-centered computing > Visualization
Human-centered computing > Visualization > Visualization application domains
Human-centered computing > Visualization > Visualization application domains > Geographic visualization
Information systems
Information systems > Information retrieval
Information systems > Information retrieval > Retrieval tasks and goals
Information systems > Information retrieval > Retrieval tasks and goals > Clustering and classification
Information systems > Information systems applications
Information systems > Information systems applications > Data mining
Information systems > Information systems applications > Data mining > Clustering
Information systems > Information systems applications > Spatial-temporal systems
ISBN:1450349935, 9781450349932
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Zusammenfassung:Density-based spatial clustering of applications with noise (DBSCAN) is the most commonly used density-based clustering algorithm, where it can discover multiple clusters with arbitrary shapes. DBSCAN works properly when the input data type is homogeneous, but the DBSCAN's approach may not be sufficient when the input dataset has textual heterogeneity (e.g., when we intend to find clusters from geo-tagged posts on social media relevant to a certain point-of-interest (POI)), thus leading to poor performance. In this paper, we present DBSTexC, a new density-based clustering algorithm using spatio--textual information on Twitter. We first define POI-relevant and POI-irrelevant tweets as the records that contain and do not contain a POI name or its coherent variations, respectively. By taking into account the fractions of POI-relevant and POI-irrelevant tweets, our DBSTexC algorithm shows a much higher clustering quality than the DBSCAN case in terms of the F1 score and its variants. DBSTexC can be thought of as a generalized version of DBSCAN due to the findings that it performs identically as DBSCAN when the inputs are homogeneous and far outperforms DBSCAN when the heterogeneous input data type is given.
ISBN:1450349935
9781450349932
DOI:10.1145/3110025.3110096