congenitally missing of maxillary lateral incisors is one of the most common patterns of hypodontia. this paper presents a nine year old boy with congenital missing of lateral incisors. familial history showed that, his mother, aunts, uncle and grandmother have also congenital absence of lateral incisors.

Many real-world medical datasets contain some proportion of missing (attribute) values. In general, missing value imputation can be performed to solve this problem, which is to provide estimations for the missing values by a reasoning process based on the (complete) observed data. However, if the observed data contain some noisy information or outliers, the estimations of the missing values may...

Missing value is an unavoidable problem when dealing with real world data sources, and various approaches for dealing with missing data have been developed. In fact, it is very important to consider the imputation ordering (ordering means which missing value should be imputed at first with the help of a specific criterion) during the imputation process, because not all attributes have the same ...

Recently, results on a comparison of seven successful methods of handling missing attribute values were reported. This paper describes experimental results on the three most successful methods out of these seven. Two of these methods, based on a Closet Fit idea (searching in a remaining data set for the closest fit case and replacing a missing attribute value by the corresponding known value fr...

A common problem encountered by many data mining techniques is the missing data. A missing data is defined as an attribute or feature in a dataset which has no associated data value. Correct treatment of these data is crucial, as they have a negative impact on the interpretation and result of data mining processes. Missing value handling techniques can be grouped into four categories, namely, c...

The problem of recovering missing values from a dataset has become an important research issue in the field of data mining and machine learning. In this thesis, we introduce an iterative missing-value completion method based on the RAR (Robust Association Rules) support values to extract useful association rules for inferring missing values in an iterative way. It consists of three phases. The ...

In analyzing data from clinical trials and longitudinal studies, the issue of missing values is always a fundamental challenge since the missing data could introduce bias and lead to erroneous statistical inferences. To deal with this challenge, several imputation methods have been developed in the literature to handle missing values where the most commonly used are complete case method, mean i...

Microarray gene expression data generally suffers from missing value problem due to a variety of experimental reasons. Since the missing data points can adversely affect downstream analysis, many algorithms have been proposed to impute missing values. In this survey, we provide a comprehensive review of existing missing value imputation algorithms, focusing on their underlying algorithmic techn...

Data mining is based on data files which usually contain errors in the form of missing values. This paper focuses on a methodological framework for the development of an automated data imputation model based on artificial neural networks. Fifteen real and simulated data sets are exposed to a perturbation experiment, based on the random generation of missing values. These data set sizes range fr...

Missing or incomplete responses are a common feature with many censuses and sample surveys. The problem created by survey non-response is that data values intended by the survey design to be observed are in fact missing. These missing values not only mean less efficient estimates because of the reduced size of the database, but also that standard complete-data methods cannot be used to analyse ...