用weka進(jìn)行數(shù)據(jù)挖掘畢業(yè)設(shè)計(jì)論文)word格式(參考版)
2025-08-10 12:02本頁(yè)面
【正文】 [1].Jiawei Han Micheline Kamber .數(shù)據(jù)挖掘概念與技術(shù).().[2]. 王娜譯. WEKA 355 Explorer 用戶指南[3]. WEKA入門教程[4]. 。通過完成這次大作業(yè)，培養(yǎng)了我獨(dú)立學(xué)習(xí)和完成任務(wù)的能力，也激發(fā)了我對(duì)數(shù)據(jù)挖掘這一課程的學(xué)習(xí)興趣。后來，我又對(duì)另外一個(gè)Nominal的數(shù)據(jù)集進(jìn)行了關(guān)聯(lián)規(guī)則挖掘，發(fā)現(xiàn)了屬性之間的關(guān)聯(lián)。包括各種挖掘工具以及結(jié)果的含義。Seed為100時(shí)： seed為50時(shí)：Seed為15時(shí)：3結(jié)論通過本次作業(yè)，一方面，深入地了解了SimpleKMeans算法的基本原理、使用范圍、不足等。彈出的窗口給出了各實(shí)例的散點(diǎn)圖。 Clustered Instances：是各個(gè)簇中實(shí)例的數(shù)目及百分比。對(duì)于數(shù)值型的屬性，簇中心就是它的均值（Mean）；分類型的就是它的眾數(shù)（Mode）， 也就是說這個(gè)屬性上取值為眾數(shù)值的實(shí)例最多。選擇Cluster,進(jìn)入聚類操作的界面，點(diǎn)擊chooose，在彈出的樹形列表中可以選擇要使用的聚類算法，再此我們使用Simlkmeans，點(diǎn)擊choose右邊的方框，在彈出的對(duì)話框中設(shè)著聚類的相關(guān)參數(shù)。在Proprocess選項(xiàng)卡，點(diǎn)擊open files,即可導(dǎo)入數(shù)據(jù)集，應(yīng)該要做一下轉(zhuǎn)換。SimpleKMeans算法是KMeans算法中最為簡(jiǎn)單的一種，也是在Weka軟件中集成的一個(gè)算法，本次大作業(yè)中，我在Weka中，利用SimpleKMeans算法對(duì)數(shù)據(jù)集進(jìn)行數(shù)據(jù)挖掘，主要是由于SimpleKMeans算法有KMeans算法應(yīng)用廣泛，具有代表性的特性，同時(shí)它有事一種最為簡(jiǎn)單的kmeans算法，更加容易我們掌握，因此，此次我選擇該算法作為我對(duì)zoo這一數(shù)據(jù)集進(jìn)行聚類分析的原因。聚類相似度是利用各聚類中對(duì)象的均值所獲得一個(gè)“中心對(duì)象”（引力中心）來進(jìn)行計(jì)算的。輸出：滿足方差最小標(biāo)準(zhǔn)的k個(gè)聚類。其中準(zhǔn)則函數(shù)定義如下：其中，E是數(shù)據(jù)集所有對(duì)象的平方誤差和，P是空間中的點(diǎn)，表示給定對(duì)象，是簇的均值。然后計(jì)算每一個(gè)簇新的均值。首先，隨機(jī)地選擇k個(gè)對(duì)象，每一個(gè)對(duì)象代表一個(gè)簇的初始均值或者中心?？梢钥醋鞔氐馁|(zhì)心。 Kmeans算法原理K均值算法以k為輸入?yún)?shù)，把n個(gè)對(duì)象的集合分成k個(gè)簇，使得結(jié)果內(nèi)的相似程度最高，而簇間的相似程度低。a139。s Guide.3. 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, worm4. Number of Instances: 1015. Number of Attributes: 18 (animal name, 15 Boolean attributes, 2 numerics)6. 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])7. Missing Attribute Values: None8. Class Distribution: Given above代碼如下：RELATION zooATTRIBUTE animal {aardvark,antelope,bass,bear,boar,buffalo,calf,carp,catfish,cavy,cheetah,chicken,chub,clam,crab,crayfish
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