What is discrimination in data?
Data discrimination, also called discrimination by algorithm, is bias that occurs when predefined data types or data sources are intentionally or unintentionally treated differently than others.
What is classification in data mining?
Classification is a data mining function that assigns items in a collection to target categories or classes. The goal of classification is to accurately predict the target class for each case in the data. For example, a classification model could be used to identify loan applicants as low, medium, or high credit risks.
What is generalization in data mining?
Data generalization is the process of creating a more broad categorization of data in a database, essentially ‘zooming out’ from the data to create a more general picture of trends or insights it provides.
What is characterization and discrimination in data mining?
Data Characterization − This refers to summarizing data of class under study. Data Discrimination − It refers to the mapping or classification of a class with some predefined group or class.
What is data bias?
The common definition of data bias is that the available data is not representative of the population or phenomenon of study. Data does not include variables that properly capture the phenomenon we want to predict. Data includes content produced by humans which may contain bias against groups of people.
What is cluster in data mining?
Clustering in Data Mining. Clustering is an unsupervised Machine Learning-based Algorithm that comprises a group of data points into clusters so that the objects belong to the same group. Each of these subsets contains data similar to each other, and these subsets are called clusters.
What is data cleaning in data mining?
Data cleaning is the process of preparing raw data for analysis by removing bad data, organizing the raw data, and filling in the null values. Ultimately, cleaning data prepares the data for the process of data mining when the most valuable information can be pulled from the data set.
How to create a step by step data collection guide?
A step-by-step guide to data collection. 1 Step 1: Define the aim of your research. Before you start the process of data collection, you need to identify exactly what you want to achieve. You 2 Step 2: Choose your data collection method. 3 Step 3: Plan your data collection procedures. 4 Step 4: Collect the data.
Which is the primary method of data collection?
Methods of data Collecting Primary Data • OBSERVATION METHOD : • Observation method is a method under which data from the field is collected with the help of observation by the observer or by personally going to the field.
What does it mean to operationalize data collection?
Operationalization means turning abstract conceptual ideas into measurable observations. When planning how you will collect data, you need to translate the conceptual definition of what you want to study into the operational definition of what you will actually measure. You have decided to use surveys to collect quantitative data.
Why do we need to collect data for research?
Whether you are performing research for business, governmental or academic purposes, data collection allows you to gain first-hand knowledge and original insights into your research problem. While methods and aims may differ between fields, the overall process of data collection remains largely the same.