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

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

TitleEvaluating the Effects of Missing Values and Mixed Data Types on Social Sequence Clustering Using t-SNE Visualization
Publication TypeJournal Article
Year of Publication2019
AuthorsAlina Lazar, Ling Jin, C. Anna Spurlock, Kesheng Wu, Alex Sim, Annika Todd
JournalJournal of Data and Information Quality
Volume11
Issue2
Pagination1 - 22
Date Published03/2019
ISSN19361955
Abstract

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 were investigated previously on single variable sequences. To understand their effects on multivariate sequence analysis, we employ a dataset of mixed data types and missing values, a dissimilarity measure designed for joint categorical sequence data, together with dimensionality reduction methodologies in a systematic design of sequence clustering experiments. Given the categorical nature of our data, we employ an “edit” distance using optimal matching. Because each data record has multiple variables of different types, we investigate the impact of mixing these variables in a single dissimilarity measure. Between variables with binary values and those with multiple nominal values, we find that the ability to overcome missing data problems is more difficult in the nominal domain than in the binary domain. Additionally, alignment of leading missing values can result in systematic biases in dissimilarity matrices and subsequently introduce both artificial clusters and unrealistic interpretations of associated data domains. We demonstrate the usage of t-distributed stochastic neighborhood embedding to visually guide mitigation of such biases by tuning the missing value substitution cost parameter or determining an optimal sequence span.

URLhttp://dl.acm.org/citation.cfm?doid=3317030http://dl.acm.org/citation.cfm?doid=3317030.3301294http://dl.acm.org/ft_gateway.cfm?id=3301294&ftid=2043165&dwn=1
DOI10.1145/331703010.1145/3301294
Short TitleJ. Data and Information QualityJDIQ