design and application of a fuzzy classified rule based on learning from fuzzy examples 基于模糊示例學(xué)習(xí)的蠓蟲分類規(guī)則的設(shè)計(jì)
it is a hotspot that the data mining of time serial model, classify rule, association rule in the data mining study currently 時(shí)間序列模式、分類規(guī)則和關(guān)聯(lián)規(guī)則挖掘是當(dāng)前數(shù)據(jù)挖掘研究中一個(gè)熱點(diǎn)。
secondly, the thesis puts forward that conditional probability of attribute to positive example can be used to compare the information which attribute provides so as to construct decision tree and to get classify rules . and a demonstration shows that the algorithm simplifies the decision tree's building process efficiently 其次,提出了利用屬性對(duì)正例的影響度來比較屬性對(duì)分類提供的信息量,進(jìn)而選擇分類屬性構(gòu)造決策樹的條件概率決策樹算法,同時(shí)實(shí)例計(jì)算說明該算法有效地簡(jiǎn)化了決策樹的生成過程。