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This document discusses data preparation and processing techniques for machine learning. It outlines topics like data cleaning, integration, transformation, and reduction. Data transformation techniques are described as converting data into an appropriate form for analysis and including methods like smoothing, aggregation, generalization, and normalization. Normalization is defined as scaling attributes to fall within a specified range. The next topic to be covered is data reduction.
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Data mining involves analyzing large amounts of data to discover patterns that can be used for purposes such as increasing sales, reducing costs, or detecting fraud. It allows companies to better understand customer behavior and develop more effective marketing strategies. Common data mining techniques used by retailers include loyalty programs to track purchasing patterns and target customers with personalized coupons. Data mining software uses techniques like classification, clustering, and prediction to analyze data from different perspectives and extract useful information and patterns.
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Dm data pre processing
This document discusses data pre-processing techniques. It describes data cleaning which detects and corrects errors in data. Data integration combines data from different sources. Data transformation converts data between formats. Data reduction simplifies data through methods like editing, scaling, encoding, sorting and tabulation. The quality of raw data affects mining results, so pre-processing aims to improve data quality and efficiency.
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7 data transformation
- 1. Data preparation and processing Mahmoud Rafeek Alfarra http://mfarra.cst.ps University College of Science & Technology- Khan yonis Development of computer systems 2016 Chapter 2 – Lecture 3
- 2. Outline Introduction Domain Expert Goal identification and Data Understanding Data Cleaning Missing values Noisy Data Inconsistent Data Data Integration Data Transformation Data Reduction Feature Selection Sampling Discretization
- 3. Introduction
- 5. Definition 1: Transform the data into a form appropriate for given data mining method. Definition 2: Data transformation is the process of converting data or information from one format to another, usually from the format of a source system into the required format of a new destination system. Data Transformation
- 6. Methods include: Smoothing Aggregation Generalization Normalization (min-max) Data Transformation
- 7. Methods of Data Transformation Normalization: Where the attributes are scaled so as to fall within a small specified ranges such as -1.0 to 1.0.
- 8. How to handle noisy data ?