Abstract
In the era of big data, a significant amount of data is produced in many applications areas. However due to various reasons including sensor failures, communication failures, environmental disruptions, and human errors, missing values are found frequently These missing data in the observed data make a challenge for other data mining approaches, requiring the missed data to be handled at the preprocessing stage of data mining. Several approaches for handling the missing data have been proposed in the past. These approaches consider the whole dataset for making a prediction, making the whole imputation approach to be cumbersome. This paper proposes the procedure which makes use of the local similarity structure of the dataset for making an Imputation. The K-means clustering technique along with the weighted KNN makes efficient imputation of the missed value. The results are compared against imputations by mean substitution and Fuzzy C Means (FCM). The proposed imputation technique shows that it performs better than other imputation procedures.
Highlights
Since the age of big data began, the collection of data from various sources, and the resultant amount of data has risen to the greatest extent [1]
Multivariate datasets are prevalent in several real-world applications, such as electrical system analysis, meteorological or economical strategy planning, security control, and plenty more
Multiple sensors are deployed to produce datasets, and they typically have one target to generate the data as activity occurs
Summary
Since the age of big data began, the collection of data from various sources, and the resultant amount of data has risen to the greatest extent [1]. Multiple sensors are deployed to produce datasets, and they typically have one target to generate the data as activity occurs. In a power grid application several sensors diagnosing the state of power transformers, produce the data by monitoring the state of gases over time [2]. In the era of IoT, a vast number of sensors are utilized for generating the multivariate environmental conditions, for example, the air or water pollution [3]. One major issue handled in the preprocessing step is missed value. The raw dataset generated by the sensor network typically includes missing values due to the rough working conditions or uncontrolled variables such as adverse weather conditions, malfunctions of the infrastructure, or unstable signals. The problem of missing data is quite prevalent in many applications. The outcome is that the data observed cannot be evaluated due to the incompleteness of the datasets
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