Statistical analysis with missing data
Statistical analysis with missing data
On the minimum probability of error of classification with incomplete patterns
Pattern Recognition
Data preparation for data mining
Data preparation for data mining
A Hybrid Neural Network System for Pattern Classification Tasks with Missing Features
IEEE Transactions on Pattern Analysis and Machine Intelligence
An efficient method for estimating null values in relational databases
Knowledge and Information Systems
Selection-fusion approach for classification of datasets with missing values
Pattern Recognition
A fuzzy c-means clustering algorithm based on nearest-neighbor intervals for incomplete data
Expert Systems with Applications: An International Journal
Rating Customers According to Their Promptness to Adopt New Products
Operations Research
A new method to determine basic probability assignment from training data
Knowledge-Based Systems
A data mining driven risk profiling method for road asset management
Proceedings of the 19th ACM SIGKDD international conference on Knowledge discovery and data mining
Dynamic discriminant functions with missing feature values
Pattern Recognition Letters
| Hi-index | 0.10 |
Missing data handling is an important preparation step for most data discrimination or mining tasks, inappropriate treatment of missing data may cause large errors or false results. In this paper, we study the effect of a missing data recovery method, namely the pseudo-nearest-neighbor substitution approach, on Gaussian distributed data sets that represent typical cases in data discrimination and data mining applications. The error rate of the proposed recovery method is evaluated by comparing the clustering results of the recovered data sets to the clustering results obtained on the originally complete data sets. The results are also compared with that obtained by applying two other missing data handling methods, the constant default value substitution and the missing data ignorance (non-substitution) methods. The experiment results provided a valuable insight to the improvement of the accuracy for data discrimination and knowledge discovery on large data sets containing missing values.