1 / 8

Revision

Revision. What is examinable?. Lectures - Week 1-7. What is evaluated?. Demonstrate understanding – from general to specific. Simple formulas Analysis/Application/Synthesis of understanding in the area. The following are only examples. Example 1 ( Application ).

seoras
Download Presentation

Revision

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. Revision

  2. What is examinable? • Lectures - Week 1-7.

  3. What is evaluated? • Demonstrate understanding – from general to specific. • Simple formulas • Analysis/Application/Synthesis of understanding in the area. • The following are only examples.

  4. Example 1 (Application) This dataset has five transactions, let min-support=60% and min_confidence=80%. Demonstrate your understanding of FP Tree algorithm by using it on the dataset above with the constraints given.

  5. Example 2 (Analysis) • Two major benefit of FP Tree is its ability to preserve completeness and compactness for frequent pattern mining. Another algorithm to mine frequent pattern is ECLAT, does it have the same benefits (mentioned above) FP-Tree algorithm?

  6. Example 3 (Application) • Given this dataset, what type of unsupervised learning algorithm would you consider running such that it produces useful information to your customers. How can you ensure that what you are providing is actual valid information?

  7. Example 4 (Comprehension) • A major problem in data stream mining is that we are unable to look at the entire stream. What would be a good technique to look at certain subset of data coming in from a stream when were are not able to look at entire stream?

  8. Example 5 (Harder Synthesis) • Describe the principles and ideas regarding the K-means algorithm. Suppose we want to include add additional constraints such that there are sets of two instances which have to be in the same clusterand sets of two instances which cannot be in the same cluster. Can we still guarantee the validity of the results?

More Related