Research Article
An Efficient Interpretable Visualization Method of Multidimensional Structural Data Matching Based on Job Seekers and Positions
Table 5
Skill and position category association rules table.
| | Antecedents | Consequents | Antecedent support | Consequent support | |
| 38 | Mathematics | Statistics | 0.408134 | 0.12923 | | 52 | Hadoop | Hive | 0.219617 | 0.141148 | | 54 | Spark | Hive | 0.255502 | 0.141148 | | 56 | Hadoop | Spark | 0.219617 | 0.255502 | | 84 | Spark, Python | Java | 0.192344 | 0.263158 | | 143 | Hadoop, Python | Spark | 0.166986 | 0.255502 | | 218 | Computer, Hadoop | Spark | 0.142105 | 0.255502 | | 240 | Hadoop, spark | Hive | 0.197608 | 0.141148 | | 246 | Machine learning, Hadoop | Spark | 0.169856 | 0.255502 | | 365 | Machine learning, Hadoop, Python | Spark | 0.13445 | 0.255502 | | 404 | Hadoop | Machine learning, computer, spark | 0.219617 | 0.141627 | | 405 | Spark | Machine learning, computer, Hadoop | 0.255502 | 0.115789 | | 40 | Spark | Hadoop | 0.282773 | 0.268067 | | 43 | HTML | CSS | 0.12605 | 0.138655 | | 80 | Spark, Python | Hadoop | 0.157143 | 0.268067 | | 82 | Hadoop, Python | Spark | 0.143277 | 0.282773 | | 88 | Hive, Java | Spark | 0.114286 | 0.282773 | | 98 | CSS, Java | HTML | 0.120168 | 0.12605 | | 122 | Spark, computer | Hadoop | 0.165546 | 0.268067 | | 128 | Spark, Hive | Hadoop | 0.195798 | 0.268067 | | 42 | C++, computer | HTML | 0.219617 | 0.141627 | | 48 | Computer, Android | Java | 0.255502 | 0.115789 | |
| | Support | Confidence | Lift | Conviction | Family | 38 | 0.107177 | 0.262603 | 2.119071 | 1.188066 | Data mining | 52 | 0.118182 | 0.538126 | 3.812488 | 1.859495 | Data mining | 54 | 0.124402 | 0.486891 | 3.449502 | 1.67382 | Data mining | 56 | 0.197608 | 0.899782 | 3.521619 | 7.428791 | Data mining | 84 | 0.103349 | 0.537313 | 2.041791 | 1.59253 | Data mining | 143 | 0.147847 | 0.885387 | 3.465278 | 6.495742 | Data mining | 218 | 0.129187 | 0.909091 | 3.558052 | 8.189474 | Data mining | 240 | 0.109091 | 0.552058 | 3.911191 | 1.917328 | Data mining | 246 | 0.156938 | 0.923944 | 3.616184 | 9.788765 | Data mining | 365 | 0.122967 | 0.914591 | 3.579578 | 8.716826 | Data mining | 404 | 0.106699 | 0.485839 | 3.430416 | 1.669463 | Data mining | 405 | 0.106699 | 0.417603 | 3.606571 | 1.518226 | Data mining | 40 | 0.218908 | 0.774146 | 2.887879 | 1.67382 | Data analysis | 43 | 0.121849 | 0.966667 | 6.971717 | 5.840336 | Data analysis | 80 | 0.118908 | 0.756684 | 2.822742 | 3.008163 | Data analysis | 82 | 0.118908 | 0.829912 | 2.934904 | 4.216799 | Data analysis | 88 | 0.1 | 0.875 | 3.094354 | 5.737815 | Data analysis | 98 | 0.105882 | 0.881119 | 6.99021 | 7.351458 | Data analysis | 122 | 0.129412 | 0.781726 | 2.916156 | 3.353273 | Data analysis | 128 | 0.153782 | 0.785408 | 2.929891 | 3.410807 | Data analysis | 42 | 0.106699 | 0.485839 | 3.430416 | 1.669463 | Software development | 48 | 0.106699 | 0.417603 | 3.606571 | 1.518226 | Software development |
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