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Machine Learning Basics for a Newbie
6 Steps of Machine learning Lifecycle Introduction to Predictive Modeling
Introduction to Exploratory Data Analysis & Data Insights Descriptive Statistics Inferential Statistics How to Understand Population Distributions?
Reading Data Files into Python Different Variable Datatypes
Probability for Data Science Basic Concepts of Probability Axioms of Probability Conditional Probability
Central Tendencies for Continuous Variables Spread of Data KDE plots for Continuous Variable Overview of Distribution for Continuous variables Normal Distribution Skewed Distribution Skeweness and Kurtosis Distribution for Continuous Variable
Central Tendencies for Categorical Variables Understanding Discrete Distributions Performing EDA on Categorical Variables
Dealing with Missing Values Understanding Outliers Identifying Outliers in Data Outlier Detection in Python Outliers Detection Using IQR, Z-score, LOF and DBSCAN
Sample and Population Central Limit Theorem Confidence Interval and Margin of Error
Bivariate Analysis Introduction
Covariance Pearson Correlation Spearman's Correlation & Kendall's Tau Correlation versus Causation Tabular and Graphical methods for Bivariate Analysis Performing Bivariate Analysis on Continuous-Continuous Variables
Tabular and Graphical methods for Continuous-Categorical Variables Introduction to Hypothesis Testing P-value Two sample Z-test T-test T-test vs Z-test Performing Bivariate Analysis on Continuous-Catagorical variables
Chi-Squares Test Bivariate Analysis on Categorical Categorical Variables
Multivariate Analysis A Comprehensive Guide to Data Exploration The Data Science behind IPL
Supervised Learning vs Unsupervised Learning Reinforcement Learning Generative and Descriminative Models Parametric and Non Parametric model
Machine Learning Pipeline Preparing Dataset Build a Benchmark Model: Regression Build a Benchmark Model: Classification
Evaluation Metrics for Machine Learning Everyone should know Confusion Matrix Accuracy Precision and Recall AUC-ROC Log Loss R2 and Adjusted R2
Dealing with Missing Values Replacing Missing Values Imputing Missing Values in Data Working with Categorical Variables Working with Outliers Preprocessing Data for Model Building
Understanding Cost Function Understanding Gradient Descent Math Behind Gradient Descent Assumptions of Linear Regression Implement Linear Regression from Scratch Train Linear Regression in Python Implementing Linear Regression in R Diagnosing Residual Plots in Linear Regression Models Generalized Linear Models Introduction to Logistic Regression Odds Ratio Implementing Logistic Regression from Scratch Introduction to Scikit-learn in Python Train Logistic Regression in python Multiclass using Logistic Regression How to use Multinomial and Ordinal Logistic Regression in R ? Challenges with Linear Regression Introduction to Regularisation Implementing Regularisation Ridge Regression Lasso Regression
Introduction to K Nearest Neighbours Determining the Right Value of K in KNN Implement KNN from Scratch Implement KNN in Python
Bias Variance Tradeoff Introduction to Overfitting and Underfitting Visualizing Overfitting and Underfitting Selecting the Right Model What is Validation? Hold-Out Validation Understanding K Fold Cross Validation
Introduction to Feature Selection Feature Selection Algorithms Missing Value Ratio Low Variance Filter High Correlation Filter Backward Feature Elimination Forward Feature Selection Implement Feature Selection in Python Implement Feature Selection in R
Introduction to Decision Tree Purity in Decision Tree Terminologies Related to Decision Tree How to Select Best Split Point in Decision Tree? Chi-Squares Information Gain Reduction in Variance Optimizing Performance of Decision Tree Train Decision Tree using Scikit Learn Pruning of Decision Trees
Introduction to Feature Engineering Feature Transformation Feature Scaling Feature Engineering Frequency Encoding Automated Feature Engineering: Feature Tools
Introduction to Naive Bayes Conditional Probability and Bayes Theorem Introduction to Bayesian Adjustment Rating: The Incredible Concept Behind Online Ratings! Working of Naive Bayes Math behind Naive Bayes Types of Naive Bayes Implementation of Naive Bayes
Understanding how to solve Multiclass and Multilabled Classification Problem Evaluation Metrics: Multi Class Classification
Introduction to Ensemble Techniques Basic Ensemble Techniques Implementing Basic Ensemble Techniques Finding Optimal Weights of Ensemble Learner using Neural Network Why Ensemble Models Work well?
Introduction to Stacking Implementing Stacking Variants of Stacking Implementing Variants of Stacking Introduction to Blending Bootstrap Sampling Introduction to Random Sampling Hyper-parameters of Random Forest Implementing Random Forest Out-of-Bag (OOB) Score in the Random Forest IPL Team Win Prediction Project Using Machine Learning Introduction to Boosting Gradient Boosting Algorithm Math behind GBM Implementing GBM in python Regularized Greedy Forests Extreme Gradient Boosting Implementing XGBM in python Tuning Hyperparameters of XGBoost in Python Implement XGBM in R/H2O Adaptive Boosting Implementing Adaptive Boosing LightGBM Implementing LightGBM in Python Catboost Implementing Catboost in Python
Different Hyperparameter Tuning methods Implementing Different Hyperparameter Tuning methods GridsearchCV RandomizedsearchCV Bayesian Optimization for Hyperparameter Tuning Hyperopt
Understanding SVM Algorithm SVM Kernels In-depth Intuition and Practical Implementation SVM Kernel Tricks Kernels and Hyperparameters in SVM Implementing SVM from Scratch in Python and R
Introduction to Principal Component Analysis Steps to Perform Principal Compound Analysis Computation of Covariance Matrix Finding Eigenvectors and Eigenvalues Implementing PCA in python Visualizing PCA A Brief Introduction to Linear Discriminant Analysis Introduction to Factor Analysis
Introduction to Clustering Applications of Clustering Evaluation Metrics for Clustering Understanding K-Means Implementation of K-Means in Python Implementation of K-Means in R Choosing Right Value for K Profiling Market Segments using K-Means Clustering Hierarchical Clustering Implementation of Hierarchial Clustering DBSCAN Defining Similarity between clusters Build Better and Accurate Clusters with Gaussian Mixture Models
Understand Basics of Recommendation Engine with Case Study
8 Ways to Improve Accuracy of Machine Learning Models
Introduction to Dask Working with CuML
Introduction to Machine Learning Interpretability Framework and Interpretable Models model Agnostic Methods for Interpretability Implementing Interpretable Model Understanding SHAP Out-of-Core ML Introduction to Interpretable Machine Learning Models Model Agnostic Methods for Interpretability Game Theory & Shapley Values
Introduction to Machine Learning Interpretability Framework and Interpretable Models model Agnostic Methods for Interpretability Implementing Interpretable Model Understanding SHAP Out-of-Core ML Introduction to Interpretable Machine Learning Models Model Agnostic Methods for Interpretability Game Theory & Shapley Values
Introduction to AutoML Implementation of MLBox Introduction to PyCaret TPOT Auto-Sklearn EvalML
Pickle and Joblib Introduction to Model Deployment
Deploying Machine Learning Model using Streamlit Deploying ML Models in Docker Deploy Using Streamlit Deploy on Heroku Deploy Using Netlify Introduction to Amazon Sagemaker Setting up Amazon SageMaker Using SageMaker Endpoint to Generate Inference Deploy on Microsoft Azure Cloud Introduction to Flask for Model Deploying ML model using Flask
Model Deployment in Android Model Deployment in Iphone

What is a Candidate Key in DBMS?

Ayushi Trivedi
Ayushi Trivedi Last Updated : 02 Jul, 2024
4 min read

Introduction

In the Database Management Systems, understanding keys is crucial for designing efficient and effective databases. Among various types of keys, candidate keys hold significant importance. A minimum superkey that uniquely identifies tuples in a relation is called a candidate key. This article will explore the idea of candidate keys, including their characteristics, significance, and ways in which they vary from other kinds of DBMS keys.

Candidate keys in DBMS

Overview

  • Understand the definition and properties of candidate keys.
  • Identify the importance of candidate keys in database design.
  • Make a distinction between super keys, foreign keys, candidate keys, and primary keys.
  • Discover how to locate potential keys within a relation.

Table of contents

What is a Candidate Keys?

A candidate key is a set of one or more attributes that can uniquely identify a tuple in a relation. Each candidate key has the following properties:

  • Uniqueness: No two distinct tuples in a relation can have the same value for the candidate key.
  • Minimality: No proper subset of the candidate key has the uniqueness property.

In simpler terms, a candidate key is a minimal superkey without any redundant attributes.

Properties of Candidate Keys

Understanding the properties of candidate keys helps in their identification and application:

  • Uniqueness: Ensures that each tuple in a relation is uniquely identifiable.
  • Minimality: Guarantees that the candidate key’s uniqueness property cannot be lost by removing any attribute from it.
  • Non-null: Candidate keys cannot contain null values as they must uniquely identify tuples.
  • Immutability: The value of a candidate key should not change over time to maintain data integrity.

Importance of Candidate Keys in DBMS

Candidate keys are essential in database design for several reasons:

  • Data Integrity: They ensure that each record in the database is unique and identifiable.
  • Normalization: By lowering redundancy and enhancing data integrity, they aid in the normalization process.
  • Indexing: They are often used for indexing, which improves the efficiency of data retrieval operations.

Candidate Key vs. Primary Key

A primary key is a special type of candidate key. Here’s how they differ:

  • Primary Key: A single candidate key chosen by the database designer to uniquely identify tuples in a relation. It cannot contain null values.
  • Candidate Key: All potential keys that can uniquely identify tuples. There can be multiple candidate keys, but only one primary key.

Example

In the Student table, both StudentID and Email could be candidate keys, but StudentID is chosen as the primary key.

StudentID (Primary Key) Name Email
1 Alice [email protected]
2 Bob [email protected]

Candidate Key vs. Super Key

A super key is a set of one or more attributes that can uniquely identify tuples in a relation. The key difference between super keys and candidate keys is minimality:

  • Super Key: Can contain additional attributes not necessary for uniqueness.
  • Candidate Key: A minimal superkey with no redundant attributes.

Example

If StudentID is a candidate key, then StudentID, Name is a super key.

StudentIDNameEmail
1Alice[email protected]
2Bob[email protected]

Here, StudentID is a candidate key, and StudentID, Name is a super key.

Candidate Key vs. Foreign Key

A foreign key is an attribute or a set of attributes in one table that uniquely identifies a row of another table or the same table. Here’s the distinction:

  • Candidate Key: Uniquely identifies tuples within its own table.
  • Foreign Key: Enables referential integrity between two tables by connecting the foreign key and primary key of one table.

Example

Consider two tables, Student and Enrollment:

Student table:

StudentID (Primary Key)Name
1Alice
2Bob

Enrollment table:

EnrollmentIDStudentID (Foreign Key)Course
1011Math
1022Science

In this case, StudentID in the Student database is referenced by StudentID in the Enrollment table, which is a foreign key.

How to Identify Candidate Keys?

Identifying candidate keys involves the following steps:

  • List All Attributes: List all attributes of the relation.
  • Determine All Possible Combinations: Consider all possible combinations of attributes.
  • Check for Uniqueness: Identify which combinations uniquely identify tuples.
  • Ensure Minimality: Eliminate any combinations that contain redundant attributes.

Example

Consider a relation Student with attributes StudentID, Name, Email, and PhoneNumber.

  • List All Attributes: StudentID, Name, Email, PhoneNumber
  • Determine All Possible Combinations: {StudentID}, {Email}, {PhoneNumber}, {StudentID, Name}, etc.
  • Check for Uniqueness: StudentID and Email uniquely identify tuples.
  • Ensure Minimality: StudentID and Email are minimal and thus candidate keys.

Examples and Use Cases

We will now look into the examples and use cases of candidate key.

Example 1: Employee Table

EmployeeID Name Email Department
E001 Alice [email protected] HR
E002 Bob [email protected] IT

In this table, EmployeeID and Email are candidate keys.

Example 2: Product Table

ProductID ProductName SerialNumber
P001 Laptop SN12345
P002 Smartphone SN67890

In this table, ProductID and SerialNumber are candidate keys.

Conclusion

A database’s efficiency and integrity are largely dependent on its candidate keys. They are essential for indexing, database normalization, and maintaining data integrity since they provide tuples with a unique identity. Database designers can greatly improve the speed and dependability of their databases by comprehending and properly applying candidate keys.

Frequently Asked Questions

Q1. Can a table have more than one candidate key in DBMS?

A. Yes, a table can have multiple candidate keys, but only one of them is chosen as the primary key.

Q2. Can candidate keys contain null values?

A. No, candidate keys must be non-null to ensure they uniquely identify tuples.

Q3. How are candidate keys related to primary keys?

A. A primary key is a candidate key chosen by the database designer to uniquely identify tuples. All primary keys are candidate keys, but not all candidate keys are primary keys.

Q4. What is the role of candidate keys in normalization?

A. Candidate keys help in normalization by identifying unique records and eliminating redundancy, thus improving data integrity and consistency.

Q5. Can a foreign key be a candidate key?

A. No, a foreign key references a primary key in another table to maintain referential integrity. It does not uniquely identify tuples within its own table.

Ayushi Trivedi
Ayushi Trivedi

My name is Ayushi Trivedi. I am a B. Tech graduate. I have 3 years of experience working as an educator and content editor. I have worked with various python libraries, like numpy, pandas, seaborn, matplotlib, scikit, imblearn, linear regression and many more. I am also an author. My first book named #turning25 has been published and is available on amazon and flipkart. Here, I am technical content editor at Analytics Vidhya. I feel proud and happy to be AVian. I have a great team to work with. I love building the bridge between the technology and the learner.

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