Missing Value Imputation Based on Gaussian Mixture Model for the Internet of Things

Abstract

This paper addresses missing value imputation for the Internet of Things (IoT). Nowadays, the IoT has been used widely and commonly by a variety of domains, such as transportation and logistics domain and healthcare domain. However, missing values are very common in the IoT for a variety of reasons, which results in the fact that the experimental data are incomplete. As a result of this, some work, which is related to the data of the IoT, can’t be carried out normally. And it leads to the reduction in the accuracy and reliability of the data analysis results. This paper, for the characteristics of the data itself and the features of missing data in IoT, divides the missing data into three types and defines three corresponding missing value imputation problems. Then, we propose three new models to solve the corresponding problems, and they are model of missing value imputation based on context and linear mean (MCL), model of missing value imputation based on binary search (MBS), and model of missing value imputation based on Gaussian mixture model (MGI). Experimental results showed that the three models can improve the accuracy, reliability, and stability of missing value imputation greatly and effectively.

Partitioning based incremental marginalization algorithm for anonymizing missing data streams

“…Anonymization of missing data streams is very challenging [29]- [31], researchers found three major techniques to treat missingness for anonymization. There are imputation, marginalization and partitioning.…”

“…Missing value is the one of the most challenging topic for data analytics [29], [30], [33], [34]. There are three main problems that missing data causes: creates substantial amount of information bias, makes data handling and analysis formidable, and inefficiency [38].…”

Missing Data Reconstruction Using Adaptively Updated Dictionary in Wireless Sensor Networks

“…The Kriging interpolation [10][11], IDW [12] [13], KNN [14], Gaussian Mixture Model [15], or RNN [16] are often used to process the geographic information. These methods are also used to estimate the missing data in WSNs.…”

Imputing the Missing Values in Iot Using Estcp Model

“…Missing data mechanism can be categorized as missing completely at random (MCAR), missing at random (MAR), not missing at random (NMAR). Finally an estimation model is to be built for the IoT depending upon its characteristics [4]. The existing machine learning algorithms don't take into account the characteristics of IoT as well as largely assume that the data is not incomplete so that all the records in the database are filled with valid values.…”

“…The existing machine learning algorithms don't take into account the characteristics of IoT as well as largely assume that the data is not incomplete so that all the records in the database are filled with valid values. Missing values are a common occurrence in IoT and can have a substantial influence on the inferences that can be drawn from the data [4]. If not imputed appropriately, it would result in imprecise, erratic analytical results.…”