Data Preprocessing Questions Long
Data reduction is a process in data preprocessing that aims to reduce the size of the dataset while preserving its essential information. It involves eliminating redundant or irrelevant data, as well as transforming the data into a more compact representation. The main goal of data reduction is to improve the efficiency and effectiveness of data analysis and storage.
There are several methods used for data compression, which is a key technique in data reduction. These methods can be broadly categorized into two types: lossless compression and lossy compression.
1. Lossless Compression:
Lossless compression techniques aim to reduce the size of the data without losing any information. The original data can be perfectly reconstructed from the compressed data. Some commonly used lossless compression methods include:
a) Run-Length Encoding (RLE): This method replaces consecutive repeated values with a count and the value itself. For example, a sequence like "AAAAABBBCCD" can be compressed to "5A3B2C1D".
b) Huffman Coding: Huffman coding assigns shorter codes to frequently occurring values and longer codes to less frequent values. This method takes advantage of the statistical properties of the data to achieve compression.
c) Arithmetic Coding: Similar to Huffman coding, arithmetic coding assigns shorter codes to more probable values. It uses fractional numbers to represent the compressed data, allowing for more efficient compression.
2. Lossy Compression:
Lossy compression techniques aim to achieve higher compression ratios by sacrificing some amount of data accuracy. The compressed data cannot be perfectly reconstructed to the original data. Some commonly used lossy compression methods include:
a) Discrete Cosine Transform (DCT): DCT is widely used in image and video compression. It transforms the data into frequency domain coefficients, discarding high-frequency components that are less perceptible to the human eye.
b) Quantization: Quantization reduces the precision of the data by mapping a range of values to a single value. This method introduces some level of distortion but achieves significant compression.
c) Principal Component Analysis (PCA): PCA is used for dimensionality reduction. It identifies the most important features in the data and discards the less significant ones, resulting in a compressed representation of the data.
It is important to note that the choice of compression method depends on the specific requirements of the application and the trade-off between compression ratio and data accuracy. Lossless compression is preferred when data integrity is crucial, while lossy compression is suitable for applications where some loss of information can be tolerated.