Frequent Itemset Mining Concepts
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Frequent Itemset Mining Concepts

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Questions and Answers

What is the main assumption when generating candidate itemsets?

  • Items must be compared by their lengths.
  • The order of items can be ignored.
  • The items in an itemset are ordered. (correct)
  • Itemsets can be unordered.

    • How is a candidate itemset of size k+1 created?

  • By randomly selecting items from Lk.
  • By joining two itemsets of size k that share the first k-1 items. (correct)
  • By duplicating an itemset of size k.
  • By merging two itemsets of different sizes.

    • When merging two itemsets, what is the condition regarding shared items?

  • They must share all items for merging to occur.
  • They do not need to share any items.
  • They must share the first k-1 items. (correct)
  • They can share only the last item.

    • Why is it important that the items are ordered in generating candidates?

    <p>It prevents itemsets from including items that appear out of order.</p> Signup and view all the answers

    Which of the following represents the correct example of merging two itemsets?

    <p>Merging (1, 2, 3) and (1, 2, 5) results in (1, 2, 3, 5).</p> Signup and view all the answers

    What does a frequent itemset indicate when its support is greater than or equal to the minsup threshold?

    <p>It consists of items that frequently appear together across transactions.</p> Signup and view all the answers

    In the context of mining frequent itemsets, what is meant by the term 'support'?

    <p>The frequency of occurrence of an itemset across transactions.</p> Signup and view all the answers

    How many possible itemsets can exist given 'd' distinct items?

    <p>2d</p> Signup and view all the answers

    What might an itemset with high frequency in a dataset potentially suggest?

    <p>That they likely represent a trend or pattern in the data.</p> Signup and view all the answers

    Which of the following is a characteristic of a frequent itemset?

    <p>It can include either one or more items.</p> Signup and view all the answers

    How does lowering the minimum support threshold affect the frequent itemsets?

    <p>It may increase the number of candidates and maximum length of frequent itemsets.</p> Signup and view all the answers

    What can happen if the dimensionality of the data set increases?

    <p>It may increase computation and I/O costs.</p> Signup and view all the answers

    What effect does the size of the database have on the Apriori algorithm?

    <p>It can lead to increased run time with more transactions.</p> Signup and view all the answers

    Why does an increase in average transaction width affect frequent itemsets?

    <p>It can lead to longer traversals of the hash tree.</p> Signup and view all the answers

    What does 'implication' refer to in the context of association rules?

    <p>The co-occurrence of items without confirming causality.</p> Signup and view all the answers

    What operation is performed to generate candidate itemsets Ck+1?

    <p>Self-join of Lk with itself</p> Signup and view all the answers

    In the SQL generation of candidates Ck+1, which condition ensures that the items are combined correctly?

    <p>p.itemk &lt; q.itemk</p> Signup and view all the answers

    Which itemset would NOT be generated as a candidate from self-joining L3={abc, abd, acd, ace, bcd}?

    <p>{b, c, d, e}</p> Signup and view all the answers

    In the example provided, what is the output of the self-join of L3 that results in {a, b, c, d}?

    <p>abcd from abc and abd</p> Signup and view all the answers

    What does L3 represent in the context of generating candidate itemsets?

    <p>The set of unique itemsets of length 3</p> Signup and view all the answers

    How many times does the itemset {Beer, Diaper} appear in the provided count list?

    <p>3</p> Signup and view all the answers

    What is the primary goal of generating candidates Ck+1 in the context of itemsets?

    <p>To expand the search space for frequent itemsets</p> Signup and view all the answers

    Which of the following itemsets counts as a candidate for {Bread,Diaper,Milk}?

    <p>{Bread, Diaper}</p> Signup and view all the answers

    Which of the following itemsets has a counter value incremented to 8?

    <p>{1 2 4}</p> Signup and view all the answers

    What is the hash function used in the example?

    <p>x mod 3</p> Signup and view all the answers

    Which itemset is NOT included in the candidate itemsets of length 3?

    <p>{5 6 8}</p> Signup and view all the answers

    In the context of the Hash Tree structure, which level of the tree corresponds to hashing on the first item?

    <p>Level 1</p> Signup and view all the answers

    What is the top level of the candidate hash tree for the itemsets?

    <p>{1 4 7}</p> Signup and view all the answers

    What does the term 'Frequent Itemsets' refer to in the A-Priori algorithm?

    <p>Itemsets that meet a minimum support threshold.</p> Signup and view all the answers

    What does the counting of itemsets in the dictionary achieve?

    <p>Filter out infrequent items.</p> Signup and view all the answers

    Which of the following itemsets is paired with a count of 0?

    <p>{3 6 8}</p> Signup and view all the answers

    How do you perform the subset operation using the hash tree?

    <p>By applying the hash function to find candidates.</p> Signup and view all the answers

    Which itemset had its counter value incremented to 2?

    <p>{2 3 4}</p> Signup and view all the answers

    What is the key operation when processing itemsets in the A-Priori algorithm?

    <p>Incrementing counters of found itemsets.</p> Signup and view all the answers

    Which of the following describes the structure of a hash tree?

    <p>A multi-level tree that stores itemsets.</p> Signup and view all the answers

    Which process involves filtering the results of candidate itemsets?

    <p>Generate L2.</p> Signup and view all the answers

    What is the main purpose of the recursive generation of itemsets?

    <p>To identify all potential frequent itemsets.</p> Signup and view all the answers

    Study Notes

    Frequent Itemset Mining

    • Application: Identify patterns in large datasets, like frequent co-occurrences.
    • Example: Finding "Brad" and "Angelina" together in many documents might indicate a relationship.
    • Frequent Itemset: A collection of items appearing together in a dataset.
      • Example: {Milk, Bread, Diaper}
    • Support (): The frequency of an itemset's occurrence.
      • Count: Number of transactions containing the itemset.
      • Fraction: Percentage of transactions containing the itemset.
    • Frequent Itemset: An itemset whose support is greater than or equal to a given minimum support threshold.
    • Mining Frequent Itemsets Task: Identify all itemsets exceeding the minimum support threshold in a set of transactions.
    • Problem Parameters:
      • N: The number of transactions.
      • d: The number of unique items.
      • w: Maximum number of items in a transaction.
    • Challenge: The number of possible itemsets grows exponentially with the number of items (2^d).
    • Solution: Utilize efficient algorithms like Apriori to find frequent itemsets.

    Apriori Algorithm

    • Assumption: Items within an itemset are ordered.
    • Candidate Generation (Ck+1): Creating itemsets of size k+1 from frequent itemsets of size k (Lk) by joining itemsets that share the first k-1 items.
    • Example: Combining {abc, abd} in L3 to generate {abcd} in C4.
    • Subset Operation using Hash Tree: A hash tree structure allows for efficient subset checking during candidate generation.
      • Hash Function: Maps items to specific branches of the tree.
      • Leafs: Store potential candidate itemsets.
      • Benefits: Reduces computation and storage requirements.

    Association Rule Mining

    • Definition: Discovering rules that predict the occurrence of one set of items based on the presence of other items in a transaction.
    • Example: "If a customer buys diapers, then they are also likely to buy beer."
    • Key Point: Implication refers to co-occurrence, not causal relationships.

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    Related Documents

    DASI_4102_Lecture_03-Itemsets_Apriori.pptx

    Description

    This quiz focuses on the principles of Frequent Itemset Mining, including definitions, applications, and challenges associated with identifying patterns in large datasets. Understand concepts such as support and itemsets while learning to handle data effectively.

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