Data Reduction Techniques

Data reduction is a method of reducing the size of original data so that it may be represented in a much smaller space. While reducing data, data reduction techniques preserve data integrity. The time spent on data reduction should not be overlooked in favour of the time saved by data mining on the smaller data set. In this part, we’ll go over data reduction in general as well as several data reduction strategies.

What is Data Reduction?

When you combine data from many data warehouses for analysis, you end up with a massive volume of data. Dealing with such a big amount of data is tough for a data analyst. Running complicated searches on large amounts of data is much more challenging since it takes a long time and often makes tracking the required data impossible. This is why data reduction is crucial. The data reduction approach decreases the amount of data while preserving its integrity. Data reduction has no effect on the outcome of data mining, which implies that the outcome of data mining before and after data reduction is the same (or almost the same).

The only variation is in the data mining efficiency. The efficiency of data mining is improved through data minimization. The approaches for data reduction will be discussed in the next section.

Data Reduction in Data Mining

Data mining is a technique for extracting information from a huge database. When dealing with large amounts of data, data analysis and mining takes a long time to complete, making it impractical and infeasible. While reducing data, data reduction techniques preserve data integrity. 

Data reduction is a method of reducing the size of original data so that it may be represented in a much smaller size. By preserving the integrity of the original data, data reduction techniques are utilised to generate a reduced version of the dataset that is substantially smaller in volume. The efficiency of the data mining process is increased by lowering the data, which delivers the same analytical conclusions.

The outcome of data mining is unaffected by data reduction. That is, the results of data mining before and after data reduction are identical or almost identical. The goal of data reduction is to make information more compact. It is easier to use sophisticated and computationally expensive algorithms when the data amount is less. The data can be reduced in terms of the number of rows (records) or the number of columns (columns) (dimensions).

Dimensionality Reduction in Data Mining

Big data refers to large-scale data sets with multi-level variables that expand at a rapid rate. The most crucial component of big data is its volume. With recent technical breakthroughs in the data processing and computer science fields, the recent expansion of big data, in terms of both the number of records and qualities, has posed a number of issues in data mining and data science in general. Multidimensional datasets are formed by extremely large data sets in big data. Having several dimensions for in a huge data collection makes evaluating them and looking for patterns in the data extremely difficult. Depending on the type of process being studied, high-dimensional data can be gathered from a variety of sources.

Any natural process is influenced by a variety of factors, some of which are observable or quantifiable and others which are not. When it comes to correctly obtaining data for any type of simulation, we must deal with higher dimensional data.

Data Reduction Techniques

•  Dimensionality Reduction

We employ the characteristic necessary for our study if we come across data that is just marginally essential. Dimensionality reduction removes characteristics from the data set in question, resulting in a reduction in the size of the original data. It shrinks data by removing obsolete or superfluous characteristics. Here are three approaches for reducing dimensionality.

• Wavelet Transform: Assume that a data vector A is transformed into a numerically different data vector A’ such that both A and A’ vectors are of the same length using the wavelet transform. Then, because the data received from the wavelet transform may be abbreviated, how is it beneficial in data reduction? By keeping the smallest piece of the strongest wavelet coefficients, compressed data can be produced. Data cubes, sparse data, and skewed data can all benefit from the Wavelet transform.

• Principal Component Analysis: Assume we have a data set with n properties that needs to be evaluated. The main component analysis finds k distinct tuples with n properties that may be used to describe the data collection. The original data may be cast on a considerably smaller space in this fashion, resulting in dimensionality reduction. Principal component analysis can be used on data that is sparse or skewed.

• sNumerosity Reduction

The numerosity reduction decreases the size of the original data and expresses it in a much more compact format. There are two sorts of numerosity reduction techniques: parametric and non-parametric.

• Parametric: Instead of keeping the original data, parmetric numerosity reduction stores just data parameters. The regression and log-linear technique is one way for reducing parametric numerosity.

• Non-Parametric: There is no model in a non-parametric numerosity reduction strategy. The non-parametric approach achieves a more uniform reduction, regardless of data size, but it may not accomplish the same volume of data reduction as the parametric technique. Histogram, Clustering, Sampling, Data Cube Aggregation, and Data Compression are at least four forms of Non-Parametric data reduction techniques.

• Data Cube Aggregation

This method is used to condense data into a more manageable format. Data Cube Aggregation is a multidimensional aggregation that represents the original data set by aggregating at multiple layers of a data cube, resulting in data reduction. 

Consider the following scenario: you have quarterly sales data for All Electronics from 2018 to 2022. To calculate the yearly sale for each year, just add up the quarterly sales for each year. Aggregation gives you with the needed data, which is considerably smaller in size, and we achieve data reduction without losing any data in this method.

• Data Compression

Data compression is the process of altering, encoding, or transforming the structure of data in order to save space. By reducing duplication and encoding data in binary form, data compression creates a compact representation of information. Lossless compression refers to data that can be effectively recovered from its compressed state. Lossy compression, on the other hand, occurs when the original form cannot be restored from the compressed version. For data compression, dimensionality and numerosity reduction methods are also utilised.

• Discretization Operation

Data discretization is a technique for dividing continuous nature qualities into data with intervals. We use labels of tiny intervals to replace several of the characteristics’ constant values. This implies that mining results are presented in a clear and succinct manner.

Conclusion 

The basic advantage of data compression is straightforward: the more data you can fit into a terabyte of disc space, the less capacity you’ll need to buy. Here are some of the advantages of data reduction: 

• Energy may be saved by reducing data.

• Data minimization might help you save money on physical storage. 

• Furthermore, data minimization might lower your data center’s track. 

Data minimization improves the efficiency of a storage system and has a direct influence on your overall capacity spending.