- An Introduction to Machine Learning | The Complete Guide
- Data Preprocessing for Machine Learning | Apply All the Steps in Python
- Regression
- 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
- Classification
- 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 in Machine Learning
- Upper Confidence Bound (UCB) Algorithm: Solving the Multi-Armed Bandit Problem
- Thompson Sampling Intuition
- Artificial Neural Networks
- Natural Language Processing
- Deep Learning
- 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
- XGBoost
- Convolution Neural Network
- Dimensionality Reduction
Eclat Intuition | Machine Learning
Written by- AionlinecourseMachine Learning Tutorials
Eclat Intuition: Today, we are talking about the Eclat model. It is similar to the Apriori 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 Apriori model we have the support vector. But we do not have confidence and lift factors. We are only looking for support. It much faster and the steps involved are set 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.