An Introduction to Machine Learning | The Complete Guide
Data Preprocessing for Machine Learning | Apply All the Steps in Python
Learn Simple Linear Regression in the Hard Way(with Python Code)
Multiple Linear Regression in Python (The Ultimate Guide)
Polynomial Regression in Two Minutes (with Python Code)
Support Vector Regression Made Easy(with Python Code)
Decision Tree Regression Made Easy (with Python Code)
Random Forest Regression in 4 Steps(with Python Code)
4 Best Metrics for Evaluating Regression Model Performance
A Beginners Guide to Logistic Regression(with Example Python Code)
K-Nearest Neighbor in 4 Steps(Code with Python & R)
Support Vector Machine(SVM) Made Easy with Python
Kernel SVM for Dummies(with Python Code)
Naive Bayes Classification Just in 3 Steps(with Python Code)
Decision Tree Classification for Dummies(with Python Code)
Random forest Classification
Evaluating Classification Model performance
A Simple Explanation of K-means Clustering in Python
Hierarchical Clustering
Association Rule Learning | Apriori
Eclat Intuition
Reinforcement Learning
Upper Confidence Bound (UCB) Algortihm: Solving the Multi-Armed Bandit Problem
Thompson Sampling Intuition
Natural Language Processing
Deep Learning
Artificial Neural Networks
Principal Component Analysis
Linear Discriminant Analysis (LDA)
Kernel PCA
Model Selection & Boosting
K-fold Cross Validation in Python | Master this State of the Art Model Evaluation Technique
Convolution Neural Network
Dimensionality Reduction

Eclat Intuition | Machine Learning

Eclat Intuition: Today, we are talking about the Eclat model. It is similar to the a priori algorithm. Here, we actually talking about the rules. Because the cloud model is different from the a priori model. In Eclat intuition, the cloud model is just like in the priori model we have the support vector. But we do not have confidence and lift factos. We are only looking for support. It much faster and the steps involved are set a minimum support so we want to set up a support level.

There are several steps:

Step 1: Set a minimum support.

Step 2: Take all the subsets in a transaction having higher support than minimum support.

Step 3: Sort these subsets by decreasing support.

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