I´m currently working on my bachelor thesis about the usage of Sequential Pattern / Sequential Rule Mining-techniques for automotive predicitve maintenance.
– >I want to answer my research question what amount of data is necessary to extract meaningful / significant Sequential patterns / rules in a sequential database.
I don´t have access to real-life automotive predictive maintenance data, so i thought about taking an webpage click-stream dataset. So my consideration is to take an sequential dataset and then gradually minimize it.
Let‘s assume that with an dataset of 1000 sequences it´s possible to extract 50 sequential rules with confidence 0,8, and with half of the data (500 sequences) it´s only able to extract 35 rules with confidence 0,8, with 50 sequences only 10 rules, and so on. I think i will only change size and variability of the data set.
Do I think correctly or are my thoughts wrong?
I would very appreciate any answer,
There are several issues that need to be addressed concretely:
To answer your question, the amount of data that is necessary is dependent on the questions mentioned above. For example, for rules of higher 'lengths', more data would be necessary.
Hope this helps.
I am not sure, but if there is a lack of data, maybe techniques as bootstart can be adequated.
As a sequence represents a collection of transactions, sequence databases have relatively less number of rows. For real world applications, small datasets are fine. What kind of nontrivial knowledge your discovering is important than size of data. I am currently working on real world applications, where data has no more than 100 lines, but the length of each sequence is very huge.
For publishing, my advice is as follows. Choose synthetic very large datasets from SPMF library to demonstrate that our model/algorithm scales well. Simultaneously, employ your real world small database to demonstrate that your model can discover useful information.
- Badges
- Science topic