Evaluating the Effects of Missing Values and Mixed Data Types on Social Sequence Clustering Using t-SNE Visualization

@article{Lazar2019EvaluatingTE,
  title={Evaluating the Effects of Missing Values and Mixed Data Types on Social Sequence Clustering Using t-SNE Visualization},
  author={Alina Lazar and Ling Jin and C. Anna Spurlock and Kesheng Wu and Alex Sim and Annika Todd},
  journal={Journal of Data and Information Quality (JDIQ)},
  year={2019},
  volume={11},
  pages={1 - 22}
}
The goal of this work is to investigate the impact of missing values in clustering joint categorical social sequences. Identifying patterns in sociodemographic longitudinal data is important in a number of social science settings. However, performing analytical operations, such as clustering on life course trajectories, is challenging due to the categorical and multidimensional nature of the data, their mixed data types, and corruption by missing and inconsistent values. Data quality issues… Expand
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