【正文】 于type1的數(shù)據(jù)較多，屬于其他type的數(shù)據(jù)相對較少，所以進行樣本平衡處理。第一次拆分確定的樣本子集隨后再次拆分，通常是根據(jù)另一個字段進行拆分，這一過程重復進行指導樣本子集不能在被拆分為止。 （2）CART：CART(Classification And Regression Tree)算法采用一種二分遞歸分割的技術，將當前的樣本集分為兩個子樣本集，使得生成的的每個非葉子節(jié)點都有兩個分支。將每個屬性的所有劃分按照他們能減少的雜質(zhì)（合成物中的異質(zhì)，不同成分）量來進行排序。AutomaticCHAID模型需要一個單一的目標和一個或多個輸入字段。CHAID算法以因變量為根結(jié)點，對每個自變量（只能是分類或有序變量，也就是離散性的，如果是連續(xù)變量，如年齡，收入要定義成分類或有序變量）進行分類，計算分類的卡方值(ChiSquareTest)。最后的每個葉結(jié)點就是一個細分市場。 【】的【模型】選項卡【】的【分析】選項卡 【Camp。R樹】的【構(gòu)建選項】選項卡（四） 【Camp。 （2）CART算法模型結(jié)果分析 該模型找出了3個影響因素:feathers,legs,airborne,其中feathers是最重要的屬性,與feathers比較，其他屬性遠遠不如feathers重要。當腿的數(shù)量等于2時，再看它是否有hair,如果沒有，則直接屬于type2，不用考慮其他因素，如果有hair,則直接屬于type1。5 模型評估 在節(jié)點工具箱的【輸出】選項卡中選擇【分析】節(jié)點，與模型結(jié)果節(jié)點相連。6 總結(jié)用數(shù)據(jù)挖掘技術對審計數(shù)據(jù)加以分析，總結(jié)出一些正常模式，用來進行異常檢測，將有助于提高入侵檢測系統(tǒng)的檢測準確性和完備性。但在自己認真的思考和查找資料，艱難的完成了這次課設。s Guide.4. Relevant Information: A simple database containing 17 Booleanvalued attributes. The type attribute appears to be the class attribute. Here is a breakdown of which animals are in which type: (I find it unusual that there are 2 instances of frog and one of girl!) Class Set of animals: 1 (41) aardvark, antelope, bear, boar, buffalo, calf, cavy, cheetah, deer, dolphin, elephant, fruitbat, giraffe, girl, goat, gorilla, hamster, hare, leopard, lion, lynx, mink, mole, mongoose, opossum, oryx, platypus, polecat, pony, porpoise, puma, pussycat, raccoon, reindeer, seal, sealion, squirrel, vampire, vole, wallaby,wolf 2 (20) chicken, crow, dove, duck, flamingo, gull, hawk, kiwi, lark, ostrich, parakeet, penguin, pheasant, rhea, skimmer, skua, sparrow, swan, vulture, wren 3 (5) pitviper, seasnake, slowworm, tortoise, tuatara 4 (13) bass, carp, catfish, chub, dogfish, haddock, herring, pike, piranha, seahorse, sole, stingray, tuna 5 (4) frog, frog, newt, toad 6 (8) flea, gnat, honeybee, housefly, ladybird, moth, termite, wasp 7 (10) clam, crab, crayfish, lobster, octopus, scorpion, seawasp, slug, starfish, worm5. Number of Instances: 1016. Number of Attributes: 18 (animal name, 15 Boolean attributes, 2 numerics)7. Attribute Information: (name of attribute and type of value domain) 1. animal name: Unique for each instance 2. hair Boolean 3. feathers Boolean 4. eggs Boolean 5. milk Boolean 6. airborne Boolean 7. aquatic Boolean 8. predator Boolean 9. toothed Boolean 10. backbone Boolean 11. breathes Boolean 12. venomous Boolean 13. fins Boolean 14. legs Numeric (set of values: {0,2,4,5,6,8}) 15. tail Boolean 16. domestic Boolean 17. catsize Boolean 18. type Numeric (integer values in range [1,7])8. Missing Attribute Values: None9. Class Distribution: Given above24