• procedure of character n-gram extraction

  • space free
  • tf-idf values

• 18 th book

  • preprocessing to same length

• ensemble of character n-grams

  • n = [3..9]

    1. not really any change over the variation of n (see gif in plots/baseline) => PCA1 = o => PCA2 = i => therefore how would it be if we do space including?

    105, 109 und 114 haben als eine der wenigen Passagen einzelne o’s wie in “Equal to this determination of yours, O Petronius” (114) elemente in Cluster 1 wie Abschnitt 4 haben einzelne i’s wie in “To be sure, I have spoken about them in the second book of the Jewish War,”

    2. It does indeed change but the results are not that well explainable anymore

       • the first 2 PCA components only explain around 5% of the variance of the dataset => space including rather not

    3. what changes do we see if we do different preprocessing?

       • remove unnecessary punctuation and keep upper/lowercase => results as with space_including
       • remove unneccesary punctuation and only lowercase => results as space_free + reasonable PCA
       • Upper/Lowercase
    4. What changes do we see if we do word n-grams
    5. What changes if employing most common ngram profile
• english text vs greek text
• different clustering solutions?

Main thought

• if we can find a certain feature set where the TF looks quite outlierish => this might be an expalantaion that this feature set is quite distinct in the TF