Sequential pattern mining is a data mining technique used to discover frequent sequences or patterns in data. I have prepared a short test of 10 questions to evaluate your knowledge of sequential pattern mining. The test is not very hard. It is just for fun.
So are you ready to test your knowledge about sequential pattern mining?
Here are 10 questions:
- What is the main goal of sequential pattern mining?
- What is the difference between sequential pattern mining and association rule mining?
- What are some common applications of sequential pattern mining?
- What is the minimum support threshold in sequential pattern mining?
- What is the difference between a sequence database and a transaction database?
- What is the difference between a sequence and a subsequence?
- What is the difference between a maximal and a closed sequential pattern?
- What is the difference between sequence and item constraints in sequential pattern mining?
- What is the difference between vertical and horizontal data formats in sequential pattern mining?
- What are some challenges in sequential pattern mining?
- The main goal of sequential pattern mining is to discover frequent sequences or patterns in a sequence database (a set of sequences).
- Association rule mining focuses on finding strong associations between items within transactions. It does not consider the time dimension or the sequential ordering between items. On the other hand, sequential pattern mining focuses on finding relationships between items across transactions over time. More precisely, sequential pattern mining aims at finding subsequences that appear frequently in a set of sequences.
- Some common applications of sequential pattern mining include market basket analysis, web usage mining, authorship attribution, malware detection, and bioinformatics.
- The minimum support threshold is a user-defined parameter that specifies the minimum number of times a sequence must appear in the data to be considered frequent.
- A sequence database contains sequences of ordered events or items, while a transaction database contains unordered sets of items.
- A sequence is an ordered list of events or items, while a subsequence is a subset of events or items from a sequence that maintains their relative order.
- A maximal sequential pattern is a frequent sequence that is not a subsequence of any other frequent sequence, while a closed sequential pattern is a frequent sequence that has no super-sequence with the same support.
- Sequence constraints specify conditions on the order and timing of events or items within a sequence, while item constraints specify conditions on the presence or absence of specific items within a sequence.
- In horizontal data format, each sequence is represented as a row ans columns are items, while in vertical data format, each row is an item and columns indicate the presence of items in sequences.
- Some challenges in sequential pattern mining include handling large datasets, dealing with noise and missing data, improving the runtime and memory consumption, and incorporating constraints. There are of course, many other challenges that could be mentioned.
How did you do on the quiz? 😊 Let me know in the comment section below!
Philippe Fournier-Viger is a professor of Computer Science and also the founder of the open-source data mining software SPMF, offering more than 180 data mining algorithms.