## Which is the most efficient method for frequent itemset mining?

BOMO algorithm presented in [7] is a frequent pattern-growth (FP-growth) based approach and known as the currently best algorithm in mining N-most interesting itemsets category. BOMO uses a compact frequent pattern-tree (FP-tree) to store compressed information about frequent itemsets.

### What is a typical example of frequent itemset mining?

A typical example of frequent itemset mining is market basket analysis. This process analyzes customer buying habits by finding associations between the different items that customers place in their “shopping baskets” (Figure 6.1).

**What are frequent itemset mining methods?**

Frequent Itemset Mining is a method for market basket analysis. It aims at finding regularities in the shopping behavior of customers of supermarkets, mail-order companies, on-line shops etc. ⬈ More specifically: Find sets of products that are frequently bought together.

**Which itemset is the most frequent What is its support in percentage )?**

B) Frequent itemset(s)

itemset | support | |
---|---|---|

1 | sunscreen | 0.500 |

2 | sandals | 0.400 |

3 | bowls | 0.200 |

4 | battery, sunscreen | 0.150 |

## How do I generate frequent itemset?

Frequent Itemset Generation

- Reduce the number of candidates: use pruning techniques such as the Apriori principle to eliminate some of the candidate itemsets without counting their support values.
- Reduce the number of transactions: by combining transactions together we can reduce the total number of transactions.

### What does frequent itemset mean?

Definition. Frequent itemsets (Agrawal et al., 1993, 1996) are a form of frequent pattern. Given examples that are sets of items and a minimum frequency, any set of items that occurs at least in the minimum number of examples is a frequent itemset.

**What is frequent itemset generation?**

A frequent itemset is an itemset whose support is greater than some user-specified minimum support (denoted Lk, where k is the size of the itemset) A candidate itemset is a potentially frequent itemset (denoted Ck, where k is the size of the itemset)

**What are frequent patterns give an example?**

Frequent patterns are itemsets, subsequences, or substructures that appear in a data set with frequency no less than a user-specified threshold. For example, a set of items, such as milk and bread, that appear frequently together in a transaction data set, is a frequent itemset.

## What do you mean by frequent itemset and closed itemset?

Definition: It is a frequent itemset that is both closed and its support is greater than or equal to minsup. An itemset is closed in a data set if there exists no superset that has the same support count as this original itemset.

### Which of the following is the direct application of frequent itemset mining?

Data processing and statistical analysis.

**How do I find closed frequent itemset?**

If we set the minsup to be 2, any itemsets that appear more than twice will be frequent itemsets. And among those frequent itemsets, we can find closed and maximal frequent itemsets by comparing their support(frequency of occurrence) to their supersets. We can see the maximal itemsets are a subset of closed itemsets.

**What is the support of frequent itemset?**

The support (or occurrence frequency) of an itemset A, where A is a set of items from I, is the number of transactions containing A in DB. An itemset A is frequent if A’s support is no less than a user-specified minimum support threshold θ. An itemset A which contains k items is called a k-itemset.

## How do you mine frequent patterns?

Mining frequent pattern with candidate generation….

- Generate Candidate set 2, do the second scan and generate Second item set.
- Generate Candidate set 3, do the third scan and generate Third item set.

### What does frequent pattern mining mean?

Definition. Frequent Pattern Mining is a Data Mining subject with the objective of extracting frequent itemsets from a database. Frequent itemsets play an essential role in many Data Mining tasks and are related to interesting patterns in data, such as Association Rules.

**What is maximal frequent itemset explain with example?**

A maximal frequent itemset is a frequent itemset for which none of its immediate supersets are frequent. To illustrate this concept, consider the example given below: The support counts are shown on the top left of each node. Assume support count threshold = 50%, that is, each item must occur in 2 or more transactions.

**What is the relation between a candidate and frequent itemset?**

11.

(a) | A candidate itemset is always a frequent itemset |
---|---|

(b) | A frequent itemset must be a candidate itemset |

(c) | No relation between these two |

(d) | Strong relation with transactions |

## What is the maximum number of frequent itemsets that can be generated from the data?

Answer: There are six items in the data set. Therefore the total number of rules is 602. (b) What is the maximum size of frequent itemsets that can be extracted (assuming minsup > 0)? Answer: Because the longest transaction contains 4 items, the maxi- mum size of frequent itemset is 4.

### What is frequent itemset?

Frequent itemsets (Agrawal et al., 1993, 1996) are a form of frequent pattern. Given examples that are sets of items and a minimum frequency, any set of items that occurs at least in the minimum number of examples is a frequent itemset.

**What do you mean by frequent itemset and strong association rule?**

Frequent itemset mining naturally leads to the discovery of associations and correlations among items in large transaction data sets. The concept of association rule was introduced together with that of frequent itemset [2]. An association rule r takes the form of α → β, where α and β are itemsets, and α ∩ β = φ.

**What is frequent pattern classification?**

9.4. 2 Discriminative Frequent Pattern–Based Classification. From work on associative classification, we see that frequent patterns reflect strong associations between attribute–value pairs (or items) in data and are useful for classification.