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Data Reduction in Data Mining

Here, we are going to learn about the Data Reduction in Data Mining.
Submitted by Palkesh Jain, on February 09, 2021

Data Reduction

Data reduction is a process that decreases the size of the original data and reflects it in a much smaller quantity. Techniques that minimize data are advantageous because they ensure the integrity of data while preserving quality. It results in a large volume of data as we obtain data from multiple data warehouses for review. This vast volume of data is daunting for a data scientist to work with. It is also difficult to perform complicated queries on the massive amount of data as it takes a long time and it is often impossible to track the correct data. That is why it becomes important to minimize data.

The strategy of data reduction decreases the data volume but retains the data integrity. The result obtained from data mining is not influenced by data reduction, which means that the result obtained from data mining is the same before and after data reduction (or almost the same). Because it is combined with a certain reason, data reduction does not make sense on its own. In turn, the aim determines the criteria for the corresponding techniques for data reduction. Decreasing the disk space is a naive aim for data reduction. This includes a process to compress the data into a more manageable format and also, as the data has to be checked, to recover the original data.

To achieve a minimized representation of the data set that is much reduced in volume but still contains important details, data reduction techniques may be applied.

  1. Aggregation of the Data Cube: In the construction of a data cube, aggregation operations are applied to the data. To compile data in a simplified way, this approach is used. For example, assume that the data we received for the report for the years 2010 to 2015 contains the company's sales every three months. Rather than the quarterly average, they include us in the yearly sales, so we can summarize the statistics in such a manner that the resulting data summarizes the cumulative sales each year instead of per fifth. It summarizes the details.
  2. Reduction in Dimension: Whenever we find some weakly meaningful results, we use the attribute needed for our study. When it replaces old or obsolete functionality, it reduces data size.
  3. Compressing Data: The technique of data compression reduces the size of files using various encoding mechanisms. Based on their compression processes, we can break it into two types.
    • Compression Lossless
    • Compression Lossy
  4. Reducing: It is necessary to store only the model parameter in this reduction technique because the real data is replaced with mathematical models or a smaller representation of the data instead of actual data. Or non-parametric methods like clustering, histogram, screening, etc.
    1. Operation for Discretization & Definition Hierarchy: Data discretization methods are used to separate the continuous nature's attributes into interval data. We substitute several constant attribute values for small interval marks. This suggests that mining effects are demonstrated in a succinct and readily understood manner.
      • Discretization of the Top-down
      • Discretization from the Bottom-up
    2. Hierarchies of Concept: By gathering and then replacing the low-level concepts with high-level concepts, this decreases the data scale (categorical variables such as middle age or Senior).

It is possible to adopt the following techniques for numeric data:

Binning - The method of changing numerical variables into categorical equivalent is called binning; the number of categorical counterparts depends on how many bins the user has defined.

Review of histograms - Like the binning process, the histogram is used to divide the value into disjoint ranges called brackets for the X attribute.

Cluster Analysis - Cluster analysis is a common form of data discretization. A clustering algorithm may be implemented by partitioning the values of A into clusters or classes to isolate a computational feature of A.

It is possible to further decompose each initial cluster or partition into many subcultures, creating a lower hierarchy level.

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