What is the purpose of data reduction
Data reduction is a process that reduced the volume of original data and represents it in a much smaller volume. Data reduction techniques ensure the integrity of data while reducing the data. Show The time required for data reduction should not overshadow the time saved by the data mining on the reduced data set. In this section, we will discuss data reduction in brief and we will discuss different methods of data reduction. Content: Data Reduction in Data Mining
What is Data Reduction?When you collect data from different data warehouses for analysis, it results in a huge amount of data. It is difficult for a data analyst to deal with this large volume of data. It is even difficult to run the complex queries on the huge amount of data as it takes a long time and sometimes it even becomes impossible to track the desired data. This is why reducing data becomes important. Data reduction technique reduces the volume of data yet maintains the integrity of the data. Data reduction does not affect the result obtained from data mining that means the result obtained from data mining before data reduction and after data reduction is the same (or almost the same). The only difference occurs in the efficiency of data mining. Data reduction increases the efficiency of data mining. In the following section, we will discuss the techniques of data reduction. Data Reduction TechniquesTechniques of data deduction include dimensionality reduction, numerosity reduction and data compression. 1. Dimensionality ReductionDimensionality reduction eliminates the attributes from the data set under consideration thereby reducing the volume of original data. In the section below, we will discuss three methods of dimensionality reduction. a. Wavelet Transform In the wavelet transform, a data vector X is transformed into a numerically different data vector X’ such that both X and X’ vectors are of the same length. Then
how it is useful in reducing data? Wavelet transform can be applied to data cube, sparse data or skewed data. b. Principal Component Analysis Let us consider we have a data set to be analyzed that has tuples with n attributes, then the principal component analysis identifies k independent tuples with n attributes that can represent the data set. In this way, the original data can be cast on a much smaller space. In this way, the dimensionality reduction can be achieved. Principal component analysis can be applied to sparse, and skewed data. c. Attribute Subset Selection The large data set has many attributes some of which are irrelevant to data mining or some are redundant. The attribute subset selection reduces the volume of data by eliminating the redundant and irrelevant attribute. The attribute subset selection makes it sure that even after eliminating the unwanted attributes we get a good subset of original attributes such that the resulting probability of data distribution is as close as possible to the original data distribution using all the attributes. 2. Numerosity ReductionThe numerosity reduction reduces the volume of the original data and represents it in a much smaller form. This technique includes two types parametric and non-parametric numerosity reduction. Parametric Parametric numerosity reduction incorporates ‘storing only data parameters instead of the original data’. One method of parametric numerosity reduction is ‘regression and log-linear’ method.
Non-Parametric
3. Data CompressionData compression is a technique where the data transformation technique is applied to the original data in order to obtain compressed data. If the compressed data can again be reconstructed to form the original data without losing any information then it is a ‘lossless’ data reduction. If you are unable to reconstruct the original data from the compressed one then your data reduction is ‘lossy’. Dimensionality and numerosity reduction method are also used for data compression. Key Takeaways
So, this is all about the data reduction and its techniques. We have covered different methods that can be employed for data reduction. What is the purpose of applying a data reduction?The purpose of data reduction can be two-fold: reduce the number of data records by eliminating invalid data; or • produce summary data and statistics at different aggregation levels for various applications.
What is data reduction and why does it matter?Data reduction achieves a reduction in volume, making it easy to represent and run data through advanced analytical algorithms. Data reduction also helps in the deduplication of data reducing the load on storage and the algorithms serving data science techniques downstream. It can be achieved in two principal ways.
What does data reduction means?Data reduction is the process of reducing the amount of capacity required to store data. Data reduction can increase storage efficiency and reduce costs. Storage vendors will often describe storage capacity in terms of raw capacity and effective capacity, which refers to data after the reduction.
What are the data reduction strategies?These are explained as following below.. Data Cube Aggregation: This technique is used to aggregate data in a simpler form. ... . Dimension reduction: ... . Data Compression: ... . Numerosity Reduction: ... . Discretization & Concept Hierarchy Operation:. |