The higher the value, the more likely the head items occur in a group if it is known that all body items are contained in that group.
Thus, the confidence of a rule is the percentage equivalent of m/n, where the values are: m.
The number of groups containing the joined rule head and rule body.
How do you calculate support and confidence?
- Support(s) –
- Support = (X+Y) total –
- Confidence(c) –
- Conf(X=>Y) = Supp(X Y) Supp(X) –
- Lift(l) –
- Lift(X=>Y) = Conf(X=>Y) Supp(Y) –
What is Apriori algorithm with example
Apriori algorithm refers to an algorithm that is used in mining frequent products sets and relevant association rules.
Generally, the apriori algorithm operates on a database containing a huge number of transactions.
For example, the items customers but at a Big Bazar.
What are the two basic steps in Apriori algorithm
Apriori algorithm was the first algorithm that was proposed for frequent itemset mining. It was later improved by R Agarwal and R Srikant and came to be known as Apriori.
This algorithm uses two steps “join” and “prune” to reduce the search space. It is an iterative approach to discover the most frequent itemsets.
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.
How do you calculate lift in data mining
Lift can be found by dividing the confidence by the unconditional probability of the consequent, or by dividing the support by the probability of the antecedent times the probability of the consequent, so: The lift for Rule 1 is (3/4)/(4/7) = (3*7)/(4 * 4) = 21/16 ≈ 1.31.
What is clustering in data mining
What is clustering in Data Mining? Clustering is the method of converting a group of abstract objects into classes of similar objects.
Clustering is a method of partitioning a set of data or objects into a set of significant subclasses called clusters.
How do you calculate support confidence and lift?
- Assume there are 100 customers
- 10 of them bought milk, 8 bought butter and 6 bought both of them
- bought milk => bought butter
- support = P(Milk & Butter) = 6/100 = 0.06
- confidence = support/P(Butter) = 0.06/0.08 = 0.75
- lift = confidence/P(Milk) = 0.75/0.10 = 7.5
How do you calculate lift ratio
To calculate the lift ratio, we divide the confidence ratio by support of consequent.
That would be 75%/60% = 1.25. It is generally considered that lift ratios higher than 1 indicate strong association between items.
Lift ratios below 1 mean that the items are not likely to be bought together.
What is Bayesian network in AI
“A Bayesian network is a probabilistic graphical model which represents a set of variables and their conditional dependencies using a directed acyclic graph.”
It is also called a Bayes network, belief network, decision network, or Bayesian model.
How do you implement Apriori algorithm in Python?
- Step 1: Import the required libraries
- Step 2: Load and explore the data
- Step 3: Clean the Data
- Step 4: Split the data according to the region of transaction
- Step 5: Hot encoding the Data
- Step 6: Build the models and analyse the results
What is the difference between classification and clustering in data mining
Differences between Classification and Clustering The process of classifying the input instances based on their corresponding class labels is known as classification whereas grouping the instances based on their similarity without the help of class labels is known as clustering.
What is confidence and support
Support is an indication of how frequently the items appear in the data. Confidence indicates the number of times the if-then statements are found true.
A third metric, called lift, can be used to compare confidence with expected confidence, or how many times an if-then statement is expected to be found true.
What is affinity diagram in Six Sigma
An affinity diagram is a tool that is used to organize a large number of ideas, opinions, and issues and group them based on their relationships.
Affinity diagrams are generally used for categorizing ideas that are generated during brainstorming sessions and can be particularly useful for analyzing complex issues.
What is an example of affinity diagram
An affinity diagram is the organization of ideas into a natural or common relationship.
For example, bananas, apples, and oranges would be grouped as fruits, while green beans, broccoli, and carrots would be grouped as vegetables.
Affinity diagrams aid teams in tapping into their creativity and gut instincts.
What is the difference between lift and confidence
Lift is nothing but the ratio of Confidence to Expected Confidence. Using the above example, expected Confidence in this case means, “confidence, if buying A and B does not enhance the probability of buying C.”
It is the number of transactions that include the consequent divided by the total number of transactions.
What is Apriori property
The Apriori property is the property showing that values of evaluation criteria of sequential patterns are smaller than or equal to those of their sequential subpatterns.
Learn more in: Sequential Pattern Mining from Sequential Data.
What is XL miner
XLMiner is a comprehensive data mining add-in for Excel. It offers a variety of methods to analyze data.
Is clustering supervised or unsupervised
Unlike supervised methods, clustering is an unsupervised method that works on datasets in which there is no outcome (target) variable nor is anything known about the relationship between the observations, that is, unlabeled data.
What is a two way lift
A two-way product lift therefore is simply a lift involving two products and can easily be computed in Excel.
It can be generalized to situations involving the computation of lifts involving more than two items or other transaction attributes (such as day of week).
What does it mean if lift is 1
A lift value near 1 indicates that the rule body and the rule head appear almost as often together as expected, this means that the occurrence of the rule body has almost no effect on the occurrence of the rule head.
What if lift is less than 1
A lift smaller than 1 indicates that the rule body and the rule head appear less often together than expected, this means that the occurrence of the rule body has a negative effect on the occurrence of the rule head.
Citations
https://www.analyticssteps.com/blogs/market-basket-analysis-overview
https://livebook.manning.com/book/machine-learning-in-action/chapter-11/
https://www.tandfonline.com/doi/full/10.1080/10042857.2013.777526
