數(shù)據(jù)挖掘概念與技術(shù)數(shù)據(jù)預處理-在線瀏覽
2024-10-12 09:43本頁面
【正文】 ectronics數(shù)據(jù)庫中用戶的購買習慣，可能會選擇下面關(guān)聯(lián)規(guī)則： P(X:customer,W)^Q(X,Y)=buys(X,Z) X是 customer表的主鍵， P,Q是謂詞變量 (在相關(guān)數(shù)據(jù)中定義 )， W,Y,Z是目標變量。挖掘相關(guān)數(shù)據(jù)可以寫成 ： use database AllElectronics_db in relevance to , , , from customer C , item I , purchase P , item_sold S where = and = and = group by 挖掘知識類型的語法 ? Mine_Knowledge_Specificaton::=Mine_Char | Mine_Discri | Mine_Assoc | Mine_Class | Mine_Pred ? Mine_Char::= Mine characterization [as pattern_name] analyze meansure(s) ? 例： mine characteristics as customerPurchasing analyze count% ? Mine_Discri::=Mine parison [as pattern_name] for target_class where target_condition { versus contrast_class_i where contrast_condition_i} analyze measure(s) ? 例： mine parison as purchaseGroups for bigSpenders where avg() ≥$100 analyze count versus budgetSpenders where avg(),$100 挖掘知識類型的語法 (續(xù) ) ? Mine_assoc::=mine association [aspattern_name] [matching metapattern] ? 例： mine associations as buyingHabits matching P(X:customer,W)^Q(X,Y)=buys(X,Z) ? Mine_class::=mine classification [as pattern_name] analyze classifying_attribute_or_dimention ? 例： mine classification as classifyingCustomerCreditRating analyze credit_info ? Mine_predi::=Mine prediction [as pattern_name] analyze prediction_attribute_or_dimention {set { attribute_or_dimention_i=value_i}} ? 例： mine prediction as predictItemPrice analyze price set category = “TV” and brand=“SONY” 概念層次語法 ? 語法： Use hierarchy hierarchy for attribute_or_dimention ? 不同概念層次采用不同定義方式 ? 模式概念層次 define hierarchy time_hierarchy on date as [date,month quarter,year] ? 集合 分組概念層次 define hierarchy age_hierarchy for age on customer as level1: {young, middle_aged, senior} level0: all level2: {20, ..., 39} level1: young level2: {40, ..., 59} level1: midd
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