fds

README.md

@@ -100,7 +100,7 @@ This attribute presents a separation of 80.96%, which is quite high, thus we cla
 
 ![Hierarchy for attribute `education`](coding-model/hierarchies/education/education.png){width=18cm}
 
-\vspace{-2em}
+\vspace{-3em}
 
 ### `education-num`
 
@@ -261,9 +261,6 @@ Measures the extent to which values are generalized. It summarizes the
 degree to which transformed attribute values cover the original domain
 of an attribute. It is equated to the converse of Granularity.
 
-We checked [2], as mentioned in ARX's help, but no useful definition of
-granularity was provided therein.
-
 ##### Classification Performance
 
 Measures how well the attributes predict the target variable
@@ -426,19 +423,19 @@ cut -d';' -f8,10 | sort | uniq -c | sort -n | column -s ';' -t
 Since there were occurences of (Wife, Male), "({Husband, Wife}, Male)"
 does not undo the transformation of the `relationship` attribute.
 
-# Citations
+# References
 
-1: Sweeney, L.: Achieving k-anonymity privacy protection
+1. Sweeney, L.: Achieving k-anonymity privacy protection
 using generalization and suppression. J. Uncertain. Fuzz. Knowl. Sys.
 10 (5), p. 571-588 (2002
 
-2: Iyengar, V.: Transforming data to satisfy privacy
+2. Iyengar, V.: Transforming data to satisfy privacy
 constraints. Proc. Int. Conf. Knowl. Disc. Data Mining, p. 279-288
 (2002)
 
-3: Bayardo, R., Agrawal, R.: Data privacy through optimal
+3. Bayardo, R., Agrawal, R.: Data privacy through optimal
 k-anonymization. Proc. Int. Conf. Data Engineering, p. 217-228 (2005).
 
-4: LeFevre, K., DeWitt, D., Ramakrishnan, R.: Mondrian
+4. LeFevre, K., DeWitt, D., Ramakrishnan, R.: Mondrian
 multidimensional k-anonymity. Proc. Int. Conf. Data Engineering
 (2006).