1. Book Classification Via Fuzzy Logic Jeremy Keer

2. If possible, use clustering ANN for a visual output
Project Goals
Develop a fuzzy logic rule set to classify the content of fantasy and science fiction books based upon genre and hardness
Allow a user to classify a book with cursory knowledge with the purpose of finding whether it is similar to other styles they have enjoyed
If possible, use clustering ANN for a visual output

3. Line between genres can be blurry at times
Why Use Fuzzy Logic?
Line between genres can be blurry at times
Existing deeper classification systems tend to rely on tags that ultimately don’t offer much information on the actual content of the story

4. Data is gathered via questionnaires
Data Set Generation
Data is gathered via questionnaires
Questions must be formulated such that answers can be given reasonably in the form of a scale
Two types of data sets used:
Shallow set answerable based largely on summary and tags
Deep set meant to be answered by someone who has read the book so that it can be accurately be classified

5. Classification Methods
Data is analyzed and given values on two spectrums:
Science Fiction vs Fantasy: while sometimes seeming to be a binary choice, tends to actually be much more complex
Hardness: hardest is “real life,” becomes progressively softer as more fantastical elements are included

6. Reducing output regions of rule set should work for classification
Expected Results
Questions work well, but would like to add more to improve classification
Finding questions that fit well to scales is the major problem
Reducing output regions of rule set should work for classification
For example, three regions on the SF/Fantasy scale and five regions for each of these for hardness