Missing value is a challenging issue in data mining, as information deficiency negatively affects both data quality and reliability. This paper focuses on an algorithm of a fuzzy clustering approach for missing value imputation with noisy data immunity. The PCFKMI (Pre-Clustering based Fuzzy K-Means Imputation) method aggregates data instances to more accurate clusters for further appropriate e...

The theory of soft set proposed by Molodtsovin 1999[1]is a new method for handling uncertain data and can be defined as a Boolean-valued information system. Ithas been applied to data analysis and decision support systems based on large datasets. In this paper, it is shown that calculated support value can be used to determine missing attribute value of an object. However, in cases when more th...

Missing value imputation is one of the biggest tasks of data pre-processing when performing data mining. Most medical datasets are usually incomplete. Simply removing the cases from the original datasets can bring more problems than solutions. A suitable method for missing value imputation can help to produce good quality datasets for better analysing clinical trials. In this paper we explore t...

The Major work in data pre-processing is handling Missing value imputation in Hepatitis Disease Diagnosis which is one of the primary stage in data mining. Many health datasets are typically imperfect. Just removing the cases from the original datasets can fetch added problems than elucidations. A appropriate technique for missing value imputation can assist to generate high-quality datasets fo...

Missing value imputation is one of the biggest tasks of data pre-processing when performing data mining. Most medical datasets are usually incomplete. Simply removing the cases from the original datasets can bring more problems than solutions. A suitable method for missing value imputation can help to produce good quality datasets for better analysing clinical trials. In this paper we explore t...

A majority of DNA microarray datasets contain missing or corrupt values and it is critical to estimate these values accurately. These missing values are most often attributed to insufficient experimental resolution or the presence of foreign objects on the experimental slide’s surface. To improve existing missing value estimation algorithms, this paper introduces and investigates the scalable s...

In order to process missing data, we propose a statistical relational learning approach for estimating and replacing missing categorical data. First, for a given data set, all categorical attributes are classified as a proper number of groups, and these groups are independent of each other. Second, principles for ordering attributes in one group are proposed and the attribute sequence of the gr...

In this paper, we study probabilistic and rough set approaches to missing attribute values. Probabilistic approaches are based on imputation, a missing attribute value is replaced either by the most probable known attribute value or by the most probable attribute value restricted to a concept. In this paper, in a rough set approach to missing attribute values we consider two interpretations of ...

DNA microarray data always contains missing values. As subsequent analysis such as biclustering can only be applied on complete data, these missing values have to be imputed before any biclusters can be detected. Existing imputation methods exploit coherence among expression values in the microarray data. In view that biclustering attempts to find correlated expression values within the data, w...

Most information systems usually have some missing values due to unavailable data. Missing values minimizing the quality of classification rules generated by a data mining system. Missing vales also affecting the quantity of classification rules achieved by the data mining system. Missing values could influence the coverage percentage and number of reducts generated. Missing values lead to the ...