Evaluating the Effects of Missing Values and Mixed Data Types on Social Sequence Clustering Using t-SNE Visualization.
Alina LazarLing JinC. Anna SpurlockKesheng WuAlex SimAnnika ToddPublished in: ACM J. Data Inf. Qual. (2019)
Keyphrases
- data types
- missing values
- high dimensional data
- missing data
- data model
- self organizing maps
- database management systems
- database systems
- incomplete data
- data structure
- clustering algorithm
- k means
- missing data imputation
- data imputation
- data points
- missing information
- data analysis
- data mining algorithms
- input data
- data clustering
- missing attribute values
- low dimensional
- preprocessing
- abstract data types
- data sets
- principal component analysis
- nearest neighbor
- low rank
- query processing
- data matrix
- feature extraction
- image processing
- databases