What AreThe Methods of Data Cleaning

Nowadays, science and technology have achieved unprecedented development. It is for this reason that a lot of science and technology have made great progress. In recent years, a lot of terms have appeared, such as big data, Internet of Things, cloud computing, artificial intelligence, etc. Among them, big data is the hottest. This is because many industries have accumulated huge raw data. Through data analysis, you can get data that is helpful to enterprise decision-making, and big data technology can be better than traditional data analysis technology. . However, big data is inseparable from data analysis, and data analysis is inseparable from data. Many of the massive data are the data we need, and there are many data we do not need. Just as there is no completely pure thing in the world, there will be impurities in the data, which requires us to clean the data to ensure the reliability of the data. Generally speaking, there is noise in the data, so how is the noise cleaned? In this article, we will introduce you to the data cleaning method.

Generally speaking, there are three methods for cleaning data, which are binning, clustering and regression. Each of these three methods has its own advantages and can clean up noise in all directions. The binning method is a commonly used method. The so-called binning method is to put the data to be processed into the box according to certain rules, and then test the data in each box, and according to the actual situation of each box in the data Circumstances take methods to process data. Seeing that many friends here only understand a little, but they don't know how to divide the boxes. How to bin? We can bin the records according to the number of rows, so that each box has the same number of records. Or we set a constant range of each box, so that we can divide the box according to the range of the box. In fact, we can also customize the interval for binning. All three methods are possible. After dividing the box numbers, we can find the average value of each box, the median, or use the extreme value to draw the line chart. Generally speaking, the greater the width of the line chart, the more obvious the smoothness.

The regression method and the binning method are also classic. The regression method is to use the data of the function to draw the image, and then smooth the image. There are two kinds of regression methods, one is single linear regression and the other is multilinear regression. Unilinear regression is to find the best straight line of two attributes and to predict one attribute from another. Multi-linear regression is to find many attributes, so as to fit the data to a multi-dimensional surface, so that the noise can be eliminated.

The workflow of the clustering method is relatively simple, but the operation is indeed complicated. The so-called clustering method is to group abstract objects into groups and form different collections. Finding the isolated points in the collection that are unexpected, these isolated points are noise. In this way, the noise can be directly found and then removed.

We introduce the data cleaning methods one by one, specifically the binning method, regression method, clustering method. Each method has its own unique advantages, which also allows data cleaning to proceed smoothly. Therefore, mastering these methods will help us to analyze the data later.