Question 4: Data Mining And Data Warehousing ✓ Solved

Pg 04question Fourdata Mining And Data Warehousingit446assignment 2de

Discuss candidate generation in Generalized Sequential Pattern (GSP) with the help of an example.

Sample Paper For Above instruction

Introduction to GSP and Candidate Generation

Generalized Sequential Pattern (GSP) is an important algorithm used in data mining for discovering frequent sequences in sequential data. It extends the Apriori principle to sequence data, aiming to identify patterns that occur frequently over time or ordered transactions. Critical to the functioning of GSP is the process of candidate generation, where potential frequent sequences are systematically generated and tested against the data to determine their support.

Understanding Candidate Generation in GSP

Candidate generation in GSP involves creating candidate sequences of increasing length from previously identified frequent sequences. This process leverages the Apriori principle: any sequence that is infrequent cannot form a part of a frequent larger sequence. Thus, candidate generation focuses on efficiently pruning unlikely sequences, reducing computational overhead. The process consists of two main steps: joining and pruning.

Step 1: Joining

In the joining step, the algorithm combines frequent sequences of length k-1 to form candidate sequences of length k. This is performed by joining sequences that share a common subsequence, ensuring that the resulting candidate maintains the sequential order. For example, if (A → B) and (A → C) are frequent sequences of length 2, they can be joined if they share a common prefix, to generate a candidate sequence (A → B → C).

Step 2: Pruning

After candidate sequences are generated, the pruning step removes those candidates that contain any subsequence of length k-1 that is not frequent. This ensures that only sequences with the potential to be frequent are retained. For example, if (A → B → C) is a candidate, its subsequences ((A → B)) and ((B → C)) are checked. If either is infrequent, then (A → B → C) is pruned.

Example of Candidate Generation

Suppose we have identified the following frequent sequences of length 2:

• (A → B)
• (A → C)
• (B → C)
• (B → D)

To generate candidate sequences of length 3, we perform joining based on shared subsequences:

- (A → B) and (A → C) can be joined if they share prefix A, resulting in (A → B → C).

- (B → C) and (B → D) can be joined if they share B, leading to (B → C → D).

Next, the pruning step eliminates candidates with infrequent subsequences. If (A → B) or (B → C) were infrequent, the larger sequences would be pruned. Assuming all their subsequences are frequent, the candidate sequences (A → B → C) and (B → C → D) are retained for support counting.

Conclusion

Candidate generation in GSP is an essential process that hinges on intelligently combining and pruning sequences based on prior knowledge of frequent subsequences. It effectively reduces computational costs by avoiding the evaluation of unlikely candidates and leverages the Apriori principle to streamline pattern discovery in sequential data.

References

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