Scalable Algorithms for Missing Value Imputation
TL;DR: Simple and efficient imputation methods based on K-means to deal with the missing data from various classes of data sets are introduced and give higher accuracy than the one given by the standard K-Means.
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Abstract: Imputation Techniques have been proposed mainly with the aim of predicting the missing values in the incomplete sets as an essential step in any data analysis framework. K-means-based Imputation, as a representative statistical imputation method, has been producing satisfied results in terms of effectiveness and efficiency in handling popular and freely available data set (e.g., Bupa, Breast Cancer, Pima, etc.). The main idea of K-means based methods is to impute the missing value relying on the prototypes of the representative class and the similarity of the data. However, such kinds of methods share the same limitations of the K- means as data mining technique. In this paper and motivated by such drawbacks, we introduce simple and efficient imputation methods based on K-means to deal with the missing data from various classes of data sets. Our proposed methods give higher accuracy than the one given by the standard K-means.
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Citations
Segmentation of nutrient deficiency in incomplete crop images using intuitionistic fuzzy C-means clustering algorithm
TL;DR: This paper introduces a new segmentation technique to segment incomplete nutrient-deficient crop images by imputing missing pixels based on intuitionistic fuzzy C-means color clustering algorithm and proves that the proposed method performs well.
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Method to Design Pattern Classification Model with Block Missing Training Data
TL;DR: Methods of pattern classification with missing data are grouped into four types: (a) deletion of incompletes, (b) insertion of new data, (c) removal of incomplete data, and (d) replacement of missing data.
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Modified K-Nearest Neighbour Using Proposed Similarity Fuzzy Measure for Missing Data Imputation on Medical Datasets (MKNNMBI)
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A.H. Alamoodi,B. B. Zaidan,B. B. Zaidan,A. A. Zaidan,Osamah Shihab Albahri,Juliana Chen,Juliana Chen,M. A. Chyad,Salem Garfan,A. M. Aleesa +9 more
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References
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John L.P. Thompson,Gilberto Levy +1 more
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TL;DR: The importance of missing data in RCTs is emphasized, and how the problem can be handled in an unbiased way by imputation procedures is discussed, and some recommendations for trial design and conduct are made that are tailored to R CTs for ALS.
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Multiple Imputation for Multivariate Missing-Data Problems: A Data Analyst's Perspective
Joseph L Schafer,Maren K. Olsen +1 more
TL;DR: The key ideas of multiple imputation are reviewed, the software programs currently available are discussed, and their use on data from the Adolescent Alcohol Prevention Trial is demonstrated.
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Impact of imputation of missing values on classification error for discrete data
TL;DR: It is shown that imputation with the tested methods on average improves classification accuracy when compared to classification without imputation, and some classifiers such as C4.5 and Nai@?ve-Bayes were found to be missing data resistant, i.e., they can produce accurate classification in the presence of missing data.
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