Table of Contents

## What is minimum support and minimum confidence in data mining?

1. Minimum support is applied to find all frequent itemsets in a data set. 2. These frequent itemsets and the minimum confidence constraint are used to compose the rules. Finding all frequent itemsets in a data set is a complex procedure since it involves analyzing all possible itemsets.

**What is support in data mining?**

Support refers to how often a given rule appears in the database being mined. Confidence refers to the amount of times a given rule turns out to be true in practice. A rule may show a strong correlation in a data set because it appears very often but may occur far less when applied.

**How do you calculate support in data mining?**

Support of the product is calculated as the ratio of the number of transactions includes that product and the total number of transactions. Confidence can be interpreted as the likelihood of purchasing both the products A and B.

### How do you calculate minimum support in association rule?

The minimum support value in the proposed method is obtained from the average utility value divided by the total existing transactions. Experiments were carried out on 8 specific datasets to determine the association rules using different dataset characteristics.

**What are the minimum support and minimum confidence used for in apriori?**

If an itemset satisfies minimum support, then it is a frequent itemset. Rules that satisfy both a minimum support threshold and a minimum confidence threshold are called strong. Apriori algorithm is an influential algorithm for mining frequent itemsets for Boolean association rules.

**What is support confidence and lift?**

For rule 1: Support says that 67% of customers purchased milk and cheese. Confidence is that 100% of the customers that bought milk also bought cheese. Lift represents the 28% increase in expectation that someone will buy cheese, when we know that they bought milk.

## What is a support count?

Support counting is the procedure of deciding the frequency of appearance for each candidate itemset that survives the candidate pruning step of the apriori-gen function.

**How do you calculate support?**

Given a set of transactions, we can find rules that will predict the occurrence of an item based on the occurrences of other items in the transaction….

- 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 support threshold?**

Minimum Support Threshold The support of an association pattern is the percentage of task-relevant data transactions for which the pattern is true. tuples. of. total. B.

### How do you make an FP tree?

The construction of a FP-tree is subdivided into three major steps.

- Scan the data set to determine the support count of each item, discard the infrequent items and sort the frequent items in decreasing order.
- Scan the data set one transaction at a time to create the FP-tree.

**How do you find support and confidence in data mining?**

Association rule mining finds interesting associations and relationships among large sets of data items. This rule shows how frequently a itemset occurs in a transaction….

- 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 does a lift of 1 mean?**

If some rule had a lift of 1, it would imply that the probability of occurrence of the antecedent and that of the consequent are independent of each other.

## How do you calculate support and resistance?

First level support and resistance:

- First resistance (R1) = (2 x PP) – Low. First support (S1) = (2 x PP) – High.
- Second resistance (R2) = PP + (High – Low) Second support (S2) = PP – (High – Low)
- Third resistance (R3) = High + 2(PP – Low) Third support (S3) = Low – 2(High – PP)

**What are the minimum support and minimum confidence used for in Apriori?**

**What is FP in data mining?**

Frequent Pattern Growth Algorithm is the method of finding frequent patterns without candidate generation. It constructs an FP Tree rather than using the generate and test strategy of Apriori. The focus of the FP Growth algorithm is on fragmenting the paths of the items and mining frequent patterns.

### What is FP algorithm?

Fp Growth Algorithm (Frequent pattern growth). FP growth algorithm is an improvement of apriori algorithm. FP growth algorithm used for finding frequent itemset in a transaction database without candidate generation. FP growth represents frequent items in frequent pattern trees or FP-tree.

**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.

**What is a good lift score?**

Then you would target all users with a score between 0.8 and 1.0, because this is the range where the churn rates are higher than the average churn rate. You don’t want to pour money down the drain for customers, who have a below-average churn probability.

## How do you calculate minimum support and confidence?

Minimum support count is the % of the all transaction. suppose you have 60% support count and 5 is the total transaction then in number the min_support will be 5*60/100=3.