| * ** Sequential pattern analysis: WarLogs Dataset. Assigned on: 02.04.2014. To be completed within: 21.04.2014. Send papers (3 pages max of text, figures excluded) by email to datamining [dot] unipi [at] gmail [dot] com. Use ”[DM] exercise 6” in the subject.** Download the Dataset here in CVS format: {{:dm:warlogs.csv.zip| warlogs.csv.zip}}. Description of the variables are [[dm:warlogs2013-14|here]]. **Problem** : Build a dataset of sequences that describe, **for each day** and **for each geographical area**, the sequence of **events** happened there. The **geographical areas** to adopt can be the same indicated in the "region" attribute already in the dataset, or they can be obtained by partitioning the territory in some other way, for instance to try to have more balanced areas. The **events** to consider can be, for instance, represented by the "category" or "type" attributes in the dataset, or they can be computed considering other informations (kind of casualties, number of wounded or killed victims, etc.). Use this dataset to extract a set of frequent sequential patterns. **Tools for sequential patterns.** Among possible alternatives, we suggest do adopt one of the following: | * ** Sequential pattern analysis: WarLogs Dataset. Assigned on: 02.04.2014. To be completed within: 21.04.2014. Send papers (3 pages max of text, figures excluded) by email to datamining [dot] unipi [at] gmail [dot] com. Use ”[DM] exercise 6” in the subject.** Download the Dataset here in CVS format: {{:dm:warlogs.csv.zip| warlogs.csv.zip}}. Description of the variables are [[dm:warlogs2013-14|here]]. **Problem** : Build a dataset of sequences that describe, **for each day** and **for each geographical area**, the sequence of **events** happened there. The **geographical areas** to adopt can be the same indicated in the "region" attribute already in the dataset, or they can be obtained by partitioning the territory in some other way, for instance to try to have more balanced areas. The **events** to consider can be, for instance, represented by the "category" or "type" attributes in the dataset, or they can be computed considering other informations (kind of casualties, number of wounded or killed victims, etc.). Use this dataset to extract a set of frequent sequential patterns. **Tools for sequential patterns.** Among possible alternatives, we suggest do adopt one of the following: |
