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基于定量關聯規則樹的分類及回歸預測算法

Categorization and regression algorithm based on the quantitative association rule tree

  • 摘要: 為了解決基于Apriori的分類關聯規則算法挖掘數值型數據時效率和準確率偏低的問題,提出基于定量關聯規則樹的分類及回歸預測算法.采用改進的定量關聯規則算法挖掘數值型數據生成關聯規則庫,并基于關聯規則樹結構實現分類及回歸預測.研究結果表明:改進的Apriori定量關聯規則挖掘算法提高了分類預測的準確率并降低了計算復雜度;而采用關聯規則樹結構可使分類與回歸預測時間明顯加快,提高了樣本匹配學習的速度.

     

    Abstract: To solve the problem of the low efficiency and accuracy of numerical data mining based on the Apriori categorization association rule algorithm,this article introduces a categorization and regression algorithm based on the quantitative association rule tree.The modified quantitative association rule algorithm is adopted to mine numerical datasets to generate an association rule base,and the association rule tree(QART) is reconstructed to realize the categorization and regression prediction.The results show that quantitative association based on the modified Apriori algorithm is helpful for improving the accuracy of categorization and regression and reducing the computational complexity,and the quantitative association rule tree can improve the efficiency of categorization and regression and increase the rule matching speed.

     

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