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What is Data Reduction?

 

Data reduction refers to the practice of taking large amounts of data and representing it in a much more compact form. While the original data is still intact, a substantially smaller version of the dataset may be obtained through the application of data reduction techniques. When the amount of data to be mined is decreased, the data mining process may be made more efficient while still yielding the same analytical outcomes. At the same time as they're saving space, data reduction methods keep your information safe. The outcome of data mining is unaffected by data reduction. That's because the outcome of data mining is identical, or almost identical, before and after data reduction.

The goal of data compression is to streamline its definition. It is much easier to implement complex and computationally expensive algorithms on smaller data sets. Both the number of rows (records) and the number of columns can be reduced when working with large datasets (dimensions). You can enroll yourself in online data science certification courses for a promising career and good pay roll. 

Techniques of Data Reduction

For instance, in data mining, you can use approaches like:

1. Dimensionality Reduction

We employ the needed property for our analysis whenever we come across weakly important data. When using dimensionality reduction, characteristics are removed from the dataset at hand, making the dataset smaller. When unnecessary information is removed, file sizes shrink. Here are three approaches to simplifying complex datasets.

  1. Wavelet Transform: When we come across data that is only moderately relevant, we make use of the attribute necessary for our study. Through the process of dimensionality reduction, the properties of the data set under review can be removed; as a result, the amount of the original data can be decreased. It accomplishes this by getting rid of features that are either obsolete or superfluous. Here are three examples: Assume for the purpose of the wavelet transform that a data vector, denoted by the letter A, is changed into a data vector, denoted by the letter A', which is numerically distinct from data vector A but has the same length as data vector A. Since the data that is acquired from the wavelet transform may be shortened, this explains how it can be valuable in the process of lowering the amount of data. The data that has been compressed is obtained by keeping the smallest portion of the wavelet coefficients that are most significant. Dimensionality reduction techniques such as data cubes, sparse data, and skewed data can all benefit from the use of wavelet transform.
  2. Principal Component Analysis: Let's say that we have a data collection that has to be studied and it contains tuples with n different properties. The principal component analysis discovers k independent tuples with n properties that might describe the data set. These tuples are then used to construct a principal component.
  3. Dimensionality reduction can then be accomplished by mapping the original data onto the significantly more condensed space that results from this process. It is possible to use principal component analysis on data that is both sparse and skewed.
  4. Attribute Subset Selection: The massive data collection has a wide variety of properties, some of which are unimportant to the process of data mining, and others of which are redundant. The data amount and dimensionality are both decreased as a result of the core attribute subset selection. Eliminating superfluous and unimportant features via the attribute subset selection helps bring the total number of data points down.
  5. The elimination of undesirable characteristics won't prevent us from having a decent subset of the original attributes, and the attribute subset selection will make sure of that. When all of the qualities are taken into account, the resultant probability of data distribution is as near as it can go to the distribution of the original data.

2. Numerosity Reduction

The original data volume is decreased and then represented in a manner that is significantly more compact thanks to the numerosity reduction. Numerosity reduction can be carried out in either a parametric or non-parametric fashion using this method.

1. Parametric: The process of saving just data parameters rather than the actual data itself is referred to as parametric numerosity reduction. The regression and log-linear technique is one approach to decreasing the number of parameters used in an analysis.

  • Regression and Log-Linear: A link between the two characteristics may be modeled using linear regression, which does so by applying a linear equation to the data set. Let's say we have to describe a linear function that connects two different properties.y = wx +b In this case, the predictor characteristic is x, while the response attribute is denoted by y. In the context of data mining, the numeric database characteristics attribute x and attribute y are referred to as x and y respectively, whereas w and b are referred to as regression coefficients.

The response variable, y, can be used to simulate a linear function between two or more predictor variables when doing multiple linear regressions.The log-linear model may determine the relationship between two or more discrete database characteristics. Let's say we have a collection of tuples that are laid out in n-dimensional space. After that, the log-linear model is applied to the problem of determining the probability of each tuple inside a multidimensional space.When dealing with sparse data or data that is skewed, you can utilise the regression or log-linear technique.

2. Non-Parametric: A strategy for reducing the numerosity of items that is non-parametric does not presuppose any model. The non-parametric method produces a reduction that is more consistent across the board, regardless of the amount of the data; but, it is possible that it will not accomplish the same level of data reduction as the parametric method. At a minimum, non-parametric data reduction methods may be broken down into the following categories: histogram, clustering, sampling, data cube aggregation, and data compression.

  • Histogram: A frequency distribution graph, often known as a histogram, is a type of graph that is used to depict how frequently a certain value appears in the data. The data distribution of an attribute may be represented as a histogram with the use of the binning method. It makes use of a non-contiguous subset, which we refer to as bins or buckets.A histogram can be used to depict data that is either dense, sparse, homogeneous, or skewed. The histogram does not have to be implemented for just one property; rather, it can be used for numerous attributes. It is possible for it to successfully represent up to five different characteristics..
  • Clustering: The purpose of clustering techniques is to group together in the data items that are very similar to one another such that the objects within a cluster are very similar to one another but are not very similar to the objects in another cluster. A distance function may be used to determine how similar the items that are contained inside a cluster are to one another. The more similar the objects in a cluster are to one another, the closer together they are located in the cluster.The diameter of the cluster, or the greatest distance that may exist between any two items that are part of the cluster, is an important factor in determining the overall quality of the cluster. The original data have been replaced with the cluster representation. If the current data can be organized into different clusters, then using this method will be far more successful.

3. Sampling: Sampling is one of the techniques that may be utilized for the purpose of reducing the amount of data that is stored, as it can transform a massive data set into a much more manageable data sample. In the following paragraphs, we will go through the many ways in which we might take a sample from a huge data collection (D) that contains N tuples:

  • Simple Random Sample Without Replacement (SRSWOR) of Size S: This s is the number of tuples that were randomly selected from a collection of N tuples such that the set of data D (s<N) has the desired information. Taking a random tuple from the set D has a chance of 1/N. As a result, every possible combination of tuples has an equal chance of being sampled.
  • Simple Random Sample With Replacement (SRSWR) of Size S: As with the SRSWOR, a tuple is selected at random from data set D, recorded, and then reinserted into the same collection of data for future use.
  • Cluster Sample: D's tuples are separated into M distinct groups. By applying SRSWOR to these clusters, we can reduce the data being stored in them. This clustering might be used to obtain a simple random sample of size s, where s<M.
  • Stratified Sample: Separate groups, or "layers," are created from the massive data collection D. To get stratified data, a simple random sample is drawn from each strata. Skewed data can be successfully analyzed using this strategy.

3. Data Cube Aggregation

This method is employed to consolidate information in a more understandable format. To achieve data reduction, a multidimensional aggregation known as "Data Cube Aggregation" aggregates data at several layers of a data cube to reflect the original data set.

Let's say, for the years 2018 through 2022, you have quarterly sales data for All Electronics. The yearly sales for any given year may be easily calculated by adding together the quarterly totals for that year. In this method, we achieve data reduction even without losing any data, as aggregation supplies you with the necessary data, which is considerably smaller in size.

Multidimensional analysis is simplified by the data cube aggregation, which combines information from many different sources. A data cube provides summarized and precomputed data, making data mining more efficient. 

4. Data Compression

To reduce storage needs, data can be compressed by being encoded, transformed, or otherwise altered to fit into a smaller container. Data compression is the process of reducing the size of a dataset by eliminating unnecessary information and encoding it as a binary string. Lossless compression is a type of data compression in which the original data may be effectively recovered from the compressed state. Lossy compression, on the other hand, is the polar opposite, where the original form cannot be restored from the compressed form. Compressing data also makes use of techniques that reduce its dimensionality and numerosity.

Files are compressed using a variety of encoding methods, including Huffman and run-length encoding, to achieve this effect. Based on the methods used for compression, we may classify it into two categories.

  • Lossless Compression: Run Length Encoding and other encoding methods provide effortless and minimum information compression. Data compression methods are used in lossless compression to allow for accurate restoration of the original data.

  • Lossy Compression: Decompressed data from lossy compression may not be identical to the originals, but they are still usable for information retrieval. JPEG is a lossy compression format, yet we can still recover information meaningfully comparable to the original image. This compression is used by methods like principal component analysis and the Discrete Wavelet transform.

5. Discretization Operation

Attributes of a continuous nature can be broken up into interval-based data using the data discretization method. Many of the properties' formerly constant values have been swapped out for interval labels. What this implies is that the data mining findings are presented in a form that is both clear and straightforward.

1. Top-down Discretization: Top-down discretization, sometimes termed splitting, is a method of dividing a set of characteristics into smaller subsets by first considering a single point or a small number of points (so-called breakpoints or split points) and then repeating this procedure until the end.

2. Bottom-up Discretization: By summing the values close to each constant value in the interval, we may eliminate some of the constants. Bottom-up discretization describes that method of operation.

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Benefits of Data Reduction in Data Science

The primary advantage of data compression is straightforward: it allows you to store more information on less storage space. A few advantages of reducing data are as follows.

  • When data is compressed, it uses less power.
  • Reducing the amount of data you need to store can help you save money on hard drive space.
  • Reducing data might also lessen the load on your data center.

In addition to lowering overall capacity costs, data minimization also improves the efficiency of your storage system.

Disadvantages of Data Reduction

The following are a few drawbacks of Dimensionality reduction:

  1. We may have degraded the quality of subsequent training methods due to the knowledge we lost during dimensionality reduction.
  2. It may require a lot of processing power.
  3. It's not always clear what's going on with transformed features.
  4. It also reduces the interpretability of the independent variables.

Conclusion

It is much easier to implement complex and computationally expensive algorithms on smaller data sets. The data that has been compressed is obtained by keeping the smallest portion of the wavelet coefficients that are most significant. When all of the qualities are taken into account, the resultant probability of data distribution is as near as it can go to the distribution of the original data. We hope you are now clear with what data reduction is, its advantages and disadvantages along with data reduction algorithms. 

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