【摘要】：現(xiàn)有的關(guān)聯(lián)規(guī)則挖掘算法沒(méi)有考慮數(shù)據(jù)流中會(huì)話的非均勻分布特性和歷史數(shù)據(jù)的作用,并且忽略了連續(xù)屬性處理時(shí)的尖銳邊界問(wèn)題。針對(duì)這些問(wèn)題,提出一種基于時(shí)間衰減模型的模糊會(huì)話關(guān)聯(lián)規(guī)則挖掘算法。針對(duì)數(shù)據(jù)流中會(huì)話的非均勻分布特性,基于時(shí)間片對(duì)會(huì)話進(jìn)行劃分,完整地保留了時(shí)間片內(nèi)會(huì)話之間的相關(guān)性信息,采用模糊集對(duì)會(huì)話的連續(xù)屬性進(jìn)行處理,增加了規(guī)則的興趣度和可理解性。在考慮歷史數(shù)據(jù)作用和允許誤差情況的基礎(chǔ)上,基于時(shí)間衰減模型挖掘數(shù)據(jù)流中的臨界頻繁項(xiàng)集和模糊關(guān)聯(lián)規(guī)則。實(shí)驗(yàn)結(jié)果表明,該方法在提高時(shí)間效率、降低冗余率和增加規(guī)則興趣度方面存在明顯優(yōu)勢(shì)。
[Abstract]:The existing association rules mining algorithms do not consider the non-uniform distribution of sessions and the role of historical data in the data flow, and ignore the sharp boundary problem in continuous attribute processing. To solve these problems, a fuzzy session association rule mining algorithm based on time attenuation model is proposed. In view of the non-uniform distribution of sessions in the data stream, the sessions are partitioned based on the time slice, and the correlation information between the sessions within the time slice is kept completely. The fuzzy set is used to deal with the continuous attributes of the session. It increases the interest and comprehensibility of the rules. The critical frequent itemsets and fuzzy association rules in the data stream are mined based on the time attenuation model on the basis of the historical data function and the allowable errors. Experimental results show that this method has obvious advantages in improving time efficiency, reducing redundancy and increasing rule interest.
【作者單位】： 解放軍信息工程大學(xué);河南省信息安全重點(diǎn)實(shí)驗(yàn)室;
【基金】：國(guó)家“863”計(jì)劃資助項(xiàng)目(2012AA012704) 國(guó)家“973”計(jì)劃資助項(xiàng)目(2011CB311801) 鄭州市科技領(lǐng)軍人才項(xiàng)目(131PLJRC644)
【分類號(hào)】：TP311.13

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