基于預(yù)判篩選的高效關(guān)聯(lián)規(guī)則挖掘算法

趙學(xué)健; 孫知信; 袁源

doi:10.11999/JEIT151107

基于預(yù)判篩選的高效關(guān)聯(lián)規(guī)則挖掘算法

doi: 10.11999/JEIT151107

基金項目:

國家自然科學(xué)基金(61373135, 61401225, 61502252, 61201160)，江蘇省基礎(chǔ)研究計劃(自然科學(xué)基金)(BK20140883, BK20140894, BK20131377)，中國博士后科學(xué)基金(2015M581844)，江蘇省博士后科研資助計劃項目(1501125B)，南京郵電大學(xué)校級科研基金(NY214101, NY215147)

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出版歷程
- 收稿日期: 2015-09-29
- 修回日期: 2016-02-26
- 刊出日期: 2016-07-19

An Efficient Association Rule Mining Algorithm Based on Prejudging and Screening

Funds:

The National Natural Science Foundation of China (61373135, 61401225, 61502252, 61201160), Natural Science Foundation of Jiangsu Province of China (BK20140883, BK20140894, BK20131377), China Postdoctoral Science Foundation Funded Project (2015M581844), Jiangsu Planned Projects for Postdoctoral Research Funds (1501125B), NUPTSF (NY214101, NY215147)

摘要

摘要: 關(guān)聯(lián)規(guī)則分析作為數(shù)據(jù)挖掘的主要手段之一，在發(fā)現(xiàn)海量事務(wù)數(shù)據(jù)中隱含的有價值信息方面具有重要的作用。該文針對Apriori 算法的固有缺陷，提出了AWP (Apriori With Prejudging) 算法。該算法在Apriori 算法連接、剪枝的基礎(chǔ)上，添加了預(yù)判篩選的步驟，使用先驗概率對候選頻繁k項集集合進行縮減優(yōu)化，并且引入阻尼因子和補償因子對預(yù)判篩選產(chǎn)生的誤差進行修正，簡化了挖掘頻繁項集的操作過程。實驗證明AWP算法能夠有效減少掃描數(shù)據(jù)庫的次數(shù)，降低算法的運行時間。
- 數(shù)據(jù)挖掘 /
- 關(guān)聯(lián)規(guī)則 /
- 事務(wù)數(shù)據(jù)庫 /
- 預(yù)判篩選 /
- Apriori
Abstract: Association rule analysis, as one of the significant means of data mining, plays an important role in discovering the implicit knowledge in massive transaction data. To overcome the inherent defects of the classic Apriori algorithm, this paper proposes Apriori With Prejudging (AWP) algorithm. AWP algorithm adds a pre-judging procedure on the basis of the self-join and pruning progress in Apriori algorithm. It reduces and optimizes the k-frequent item sets using prior probability. In addition, the damping factor and compensating factor are introduced to revise the deviation caused by pre-judging. AWP algorithm simplifies the operation process of mining frequent item sets. Experimental results show that the improvement measures can effectively reduce the number of scanning databases and reduce the running time of the algorithm.
- Data mining /
- Association rules /
- Transaction database /
- Prejudging; Apriori /

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參考文獻(19)

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