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dm:start:guidelines

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dm:start:guidelines [05/05/2017 alle 08:24 (5 anni fa)]
Anna Monreale [Guidelines for the task on Association Rules Mining]
dm:start:guidelines [23/11/2020 alle 10:34 (14 mesi fa)] (versione attuale)
Riccardo Guidotti [Guidelines for the task on Classification]
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 ====== Guidelines for the task on Data Understanding ====== ====== Guidelines for the task on Data Understanding ======
    * Data understanding (30 points)    * Data understanding (30 points)
-   Data semantics (3 points) +     Data semantics (3 points) 
-   * Distribution of the variables and statistics (7 points) +     - Distribution of the variables and statistics (7 points) 
-   * Assessing data quality (missing values, outliers) (7 points) +     - Assessing data quality (missing values, outliers) (7 points) 
-   * Variables transformations (6 points) +     - Variables transformations (6 points) 
-   * Pairwise correlations and eventual elimination of redundant variables (7 points)+     - Pairwise correlations and eventual elimination of redundant variables (7 points)
  
    
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 ====== Guidelines for the task on Association Rules Mining ====== ====== Guidelines for the task on Association Rules Mining ======
-  * Frequent patterns extraction with different values of support and different types  (i.e. frequent, close maximal), (points) +  * Frequent patterns extraction with different values of support and different types (i.e. frequent, closemaximal), (points) 
-  * Discussion of the most interesting frequent patterns (points) +  * Discussion of the most interesting frequent patterns and analyze how changes the number of patterns w.r.t. the min_sup parameter (points) 
-  * Association rules extraction with different values of confidence (points) +  * Association rules extraction with different values of confidence (points) 
-  * Discussion of the most interesting rules (points) +  * Discussion of the most interesting rules and analyze how changes the number of rules w.r.t. the min_conf parameter, histogram of rules' confidence and lift (points) 
-  * Use the most meaningful rules to replace missing values and evaluate the accuracy  (points) +  * Use the most meaningful rules to replace missing values and evaluate the accuracy (points) 
-  * Use the most meaningful rules to predict if the diabetes is detected and evaluate the accuracy (points)+  * Use the most meaningful rules to predict the target variable and evaluate the accuracy (points)
  
  
 ====== Guidelines for the task on Classification ====== ====== Guidelines for the task on Classification ======
-   * Learning of different decision trees with different parameters and gain formulas with the object of maximizing the performances (12 points) +   * Learning of different decision trees/classification algorithms with different parameters and gain formulas with the object of maximizing the performances (12 points) 
-   * Decision trees interpretation (6 points) +   * Decision trees interpretation, validation with test and training set (6 points)  
-   Decision trees validation with test and training set (6 points)+   Training of different KNN classifiers with different parameters with the object of maximizing the performances (6 points)
    * Discussion of the best prediction model (6 points)    * Discussion of the best prediction model (6 points)
  
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    * Only PDF file are allowed, you do not have to submit python code or the knime workflows.    * Only PDF file are allowed, you do not have to submit python code or the knime workflows.
    * The final paper must be easily readable, i.e., it is better to use font size higher than 9pt.    * The final paper must be easily readable, i.e., it is better to use font size higher than 9pt.
-   * Use a readable font size, e.g. Arial, Times New Romans+   * Use a readable font type and size, e.g. Arial, Times New Romans
    * You can use multiple columns and change the margin size but the project must be readable.    * You can use multiple columns and change the margin size but the project must be readable.
    * It is NOT required to put python code, knime flows, or theoretical descriptions of the algorithm in the final paper.    * It is NOT required to put python code, knime flows, or theoretical descriptions of the algorithm in the final paper.
dm/start/guidelines.1493972683.txt.gz · Ultima modifica: 05/05/2017 alle 08:24 (5 anni fa) da Anna Monreale