Statistical analysis with missing data
Statistical analysis with missing data
C4.5: programs for machine learning
C4.5: programs for machine learning
Data preparation for data mining
Data preparation for data mining
A General Additive Data Perturbation Method for Database Security
Management Science
Machine Learning
Data Swapping: Balancing Privacy against Precision in Mining for Logic Rules
DaWaK '99 Proceedings of the First International Conference on Data Warehousing and Knowledge Discovery
Data Mining: Concepts and Techniques
Data Mining: Concepts and Techniques
Cleaning uncertain data with quality guarantees
Proceedings of the VLDB Endowment
AAAI'05 Proceedings of the 20th national conference on Artificial intelligence - Volume 2
Improved use of continuous attributes in C4.5
Journal of Artificial Intelligence Research
EXPLORE: a novel decision tree classification algorithm
BNCOD'10 Proceedings of the 27th British national conference on Data Security and Security Data
An enhanced secure preserving for pre-processed data using DMI and PCRBAC algorithm
Proceedings of the Second International Conference on Computational Science, Engineering and Information Technology
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Data pre-processing plays a vital role in data mining for ensuring good quality of data. In general data pre-processing tasks include imputation of missing values, identification of outliers, smoothening out of noisy data and correction of inconsistent data. In this paper, we present an efficient missing value imputation technique called DMI, which makes use of a decision tree and expectation maximization (EM) algorithm. We argue that the correlations among attributes within a horizontal partition of a data set can be higher than the correlations over the whole data set. For some existing algorithms such as EM based imputation (EMI) accuracy of imputation is expected to be better for a data set having higher correlations than a data set having lower correlations. Therefore, our technique (DMI) applies EMI on various horizontal segments (of a data set) where correlations among attributes are high. We evaluate DMI on two publicly available natural data sets by comparing its performance with the performance of EMI. We use various patterns of missing values each having different missing ratios up to 10%. Several evaluation criteria such as coefficient of determination (R2), Index of agreement (d2) and root mean squared error (RMSE) are used. Our initial experimental results indicate that DMI performs significantly better than EMI.