Each student will create a new wiki entry from a peer-revie…
Each student will create a new wiki entry from a peer-reviewed research paper that pertains subject below, or provide a summary or substantive commentary on an existing WIKI entry from a classmate. Admin Notes: Conduct your own research and follow post a short relevant summary of your findings. ( Post current information, not older than five years ). Use not more than three (3) references. From Chapter 7 Association Rules: Advanced Concepts and Algorithms Post a relevant summary about one (1) of the followings topics : i choosed discuss generalized sequential pattern (gsp) just need 1 page of information about the topic as per instructions
Topic: Generalized Sequential Pattern (GSP)
Generalized sequential pattern mining is a data mining technique used to discover recurring patterns in sequential data. This concept extends the well-known sequential pattern mining approach by allowing the discovery of patterns across multiple dimensions or levels of abstraction. In other words, GSP algorithms aim to identify patterns that occur not only in a single sequence but also in multiple sequences or databases.
The primary goal of GSP is to find frequent sequential patterns in a given dataset. A sequential pattern refers to a set of items or events that occur within a specific order and time sequence. Frequent sequential patterns are those patterns that occur frequently enough to be considered interesting or significant. By mining these patterns, valuable insights can be gained from various applications such as market basket analysis, web clickstream analysis, and DNA sequence analysis.
GSP algorithms utilize a depth-first search strategy to efficiently discover frequent sequential patterns. The search process begins with generating a set of frequent 1-item sequences, which represent the individual items that occur frequently in the dataset. Subsequently, the algorithm iteratively expands the current sequence by appending new items to form longer sequences. These longer sequences are then checked against the minimum support threshold to determine their frequency. If a sequence satisfies the minimum support threshold, it is considered a frequent sequence. The process continues until no more frequent sequences can be generated.
One key advantage of GSP algorithms is their ability to handle variable-length sequences, where the length of patterns can vary. This flexibility allows GSP algorithms to handle diverse types of sequential data. Another advantage is their scalability, as GSP algorithms can efficiently process large datasets by exploiting various optimization techniques like pruning strategies and vertical representation.
However, there are some challenges associated with generalized sequential pattern mining. One challenge is the combinatorial explosion of possible patterns as the sequence length increases. This can result in an exponential increase in the number of candidate patterns, making the mining process computationally expensive. To address this issue, various pruning techniques and heuristics have been proposed to reduce the search space and improve the efficiency of mining algorithms.
In summary, generalized sequential pattern mining is a valuable technique for discovering frequent patterns in sequential data. These patterns can provide valuable insights into various domains and applications. GSP algorithms leverage depth-first search strategies and optimization techniques to efficiently mine patterns from variable-length sequences. However, challenges such as the combinatorial explosion of patterns need to be carefully addressed to ensure the scalability and effectiveness of GSP algorithms.