Non-Parametric Methods –
These methods are used for storing reduced representations of the data include histograms, clustering, sampling and data cube aggregation. Histograms: Histogram is the data representation in terms of frequency. It uses binning to approximate data distribution and is a popular form of data reduction. Clustering: Clustering divides the data into groups/clusters. This technique partitions the whole data into different clusters. In data reduction, the cluster representation of the data are used to replace the actual data. It also helps to detect outliers in data. Sampling: Sampling can be used for data reduction because it allows a large data set to be represented by a much smaller random data sample (or subset). Data Cube Aggregation: Data cube aggregation involves moving the data from detailed level to a fewer number of dimensions. The resulting data set is smaller in volume, without loss of information necessary for the analysis task.
ADVANTAGES OR DISADVANTAGES:
Numerosity reduction can have both advantages and disadvantages when used in data mining:
Numerosity Reduction in Data Mining
Prerequisite: Data preprocessing Why Data Reduction ? Data reduction process reduces the size of data and makes it suitable and feasible for analysis. In the reduction process, integrity of the data must be preserved and data volume is reduced. There are many techniques that can be used for data reduction. Numerosity reduction is one of them. Numerosity Reduction: Numerosity Reduction is a data reduction technique which replaces the original data by smaller form of data representation. There are two techniques for numerosity reduction- Parametric and Non-Parametric methods.
INTRODUCTION:
Numerosity reduction is a technique used in data mining to reduce the number of data points in a dataset while still preserving the most important information. This can be beneficial in situations where the dataset is too large to be processed efficiently, or where the dataset contains a large amount of irrelevant or redundant data points.
There are several different numerosity reduction techniques that can be used in data mining, including:
- Data Sampling: This technique involves selecting a subset of the data points to work with, rather than using the entire dataset. This can be useful for reducing the size of a dataset while still preserving the overall trends and patterns in the data.
- Clustering: This technique involves grouping similar data points together and then representing each group by a single representative data point.
- Data Aggregation: This technique involves combining multiple data points into a single data point by applying a summarization function.
- Data Generalization: This technique involves replacing a data point with a more general data point that still preserves the important information.
- Data Compression: This technique involves using techniques such as lossy or lossless compression to reduce the size of a dataset.
- It’s important to note that numerosity reduction can have a trade-off between the accuracy and the size of the data. The more data points are reduced, the less accurate the model will be and the less generalizable it will be.
In conclusion, numerosity reduction is an important step in data mining, as it can help to improve the efficiency and performance of machine learning algorithms by reducing the number of data points in a dataset. However, it is important to be aware of the trade-off between the size and accuracy of the data, and carefully assess the risks and benefits before implementing it.
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