Statistical analysis with missing data
Statistical analysis with missing data
Discovering data mining: from concept to implementation
Discovering data mining: from concept to implementation
ACM Computing Surveys (CSUR)
Data mining: concepts and techniques
Data mining: concepts and techniques
Feature Extraction, Construction and Selection: A Data Mining Perspective
Feature Extraction, Construction and Selection: A Data Mining Perspective
Data Mining: Concepts, Models, Methods and Algorithms
Data Mining: Concepts, Models, Methods and Algorithms
Imputation of Missing Data in Industrial Databases
Applied Intelligence
Machine Learning
ICML '02 Proceedings of the Nineteenth International Conference on Machine Learning
Semi-supervised Clustering by Seeding
ICML '02 Proceedings of the Nineteenth International Conference on Machine Learning
Expert Constrained Clustering: A Symbolic Approach
PKDD '00 Proceedings of the 4th European Conference on Principles of Data Mining and Knowledge Discovery
Intelligent clustering with instance-level constraints
Intelligent clustering with instance-level constraints
Knowledge Discovery through Mining Emergency Department Data
HICSS '05 Proceedings of the Proceedings of the 38th Annual Hawaii International Conference on System Sciences (HICSS'05) - Track 6 - Volume 06
A Missing Data Estimation Analysis in Type II Diabetes Databases
CBMS '05 Proceedings of the 18th IEEE Symposium on Computer-Based Medical Systems
Semi-Supervised Clustering Models for Clinical Risk Assessment
BIBE '06 Proceedings of the Sixth IEEE Symposium on BionInformatics and BioEngineering
GBKII: an imputation method for missing values
PAKDD'07 Proceedings of the 11th Pacific-Asia conference on Advances in knowledge discovery and data mining
AAAI'96 Proceedings of the thirteenth national conference on Artificial intelligence - Volume 1
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Ongoing research and development process in medical data mining have opened up versatile computer assisted approaches for effective clinical decisions. The nature and quality of the selected sample for training is largely responsible for the performance of the data mining algorithms. The large quantities of cumulative data collected from various sources suffer from qualitative deficiency factors such as inconsistency, incompleteness and redundancy. Addressing the prime problem of missing data is vital as it may introduce a bias into the model under evaluation, at times leading to inaccurate results. Imputation of missing data through instance-based clustering methodology is proposed in this paper. A complete dataset, Pima Indian Type II Diabetes, is considered for evaluation of the proposed method and its usefulness and performance are estimated through average imputation error E. The results illustrate that the proposed clustering method gives a lesser and stable error rate compared to other existing imputation methods.