基于“新穎度”的關(guān)聯(lián)挖掘算法An Association R

關(guān)聯(lián)挖掘的目的是從大量數(shù)據(jù)中發(fā)現(xiàn)對用戶有用、新穎、重要的關(guān)聯(lián)規(guī)則。傳統(tǒng)的關(guān)聯(lián)挖掘算法會產(chǎn)生大量對用戶而言顯而易見的平凡規(guī)則，使那些真正對用戶有用的新穎規(guī)則被淹沒，而一些針對新穎性的改進(jìn)算法往往又存在先驗(yàn)知識表達(dá)復(fù)雜且工作量極大的問題。在本文中，我們運(yùn)用簡單的分類樹，引入“新穎度”的概念，對Apriori 算法進(jìn)行改進(jìn)，得到了基于“新穎度”的關(guān)聯(lián)挖掘算法，此算法既充分考慮了挖掘過程中得新穎性問題，又克服了先驗(yàn)知識表達(dá)過于復(fù)雜的困難。
關(guān)鍵詞：關(guān)聯(lián)規(guī)則挖掘；Apriori 算法；新穎度

Abstract: The objective of mining association rules is to find useful, novel and important association rules from large database. Traditional association rule mining algorithm may often produce too many obvious and non-novel rules to user, making really novel and interesting rules submerged. Some former way to solve novel problem need a lot of extra work to preprocess the data. In this paper, we introduce the concept of “novelty” and improve the algorithm into a new association algorithm based on “novelty” to overcome the above problems.
Key words: Association Rule mining; Apriori Algorithm; Novelty