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數(shù)據(jù)挖掘 版權(quán)信息
- ISBN：9787121335310
- 條形碼：9787121335310 ; 978-7-121-33531-0
- 裝幀：一般膠版紙
- 冊(cè)數(shù)：暫無
- 重量：暫無
- 所屬分類：>>
數(shù)據(jù)挖掘 本書特色
中國(guó)大數(shù)據(jù)專家委員會(huì)劉鵬教授聯(lián)合全國(guó)上百家高校從事一線教學(xué)科研任務(wù)的教師，一起編撰高級(jí)大數(shù)據(jù)人才培養(yǎng)從書。本書是叢書之一，其定位是大數(shù)據(jù)挖掘技術(shù)與應(yīng)用。本書系統(tǒng)地介紹了數(shù)據(jù)挖掘算法理論與方法、工具和應(yīng)用，包括經(jīng)典數(shù)據(jù)挖掘算法，大數(shù)據(jù)環(huán)境下常用數(shù)據(jù)挖掘算法的優(yōu)化，大數(shù)據(jù)新常態(tài)下催生的數(shù)據(jù)分析方法（如推薦系統(tǒng)、鏈接分析與網(wǎng)頁排序、互聯(lián)網(wǎng)信息抽取、日志挖掘與查詢分析）、工具與應(yīng)用。本書適合作為相關(guān)專業(yè)本科和研究生教材。高職高專學(xué)校也可以選用部分內(nèi)容開展教學(xué)。本書也很適合作為大數(shù)據(jù)分析研發(fā)人員的自學(xué)書籍。
數(shù)據(jù)挖掘 內(nèi)容簡(jiǎn)介
短短幾年間，大數(shù)據(jù)就以一日千里的發(fā)展速度，快速實(shí)現(xiàn)了從概念到落地，直接帶
動(dòng)了相關(guān)產(chǎn)業(yè)井噴式發(fā)展。全球多家研究機(jī)構(gòu)統(tǒng)計(jì)數(shù)據(jù)顯示，大數(shù)據(jù)產(chǎn)業(yè)將迎來發(fā)展黃
金期：IDC 預(yù)計(jì)，大數(shù)據(jù)和分析市場(chǎng)將從2016 年的1300 億美元增長(zhǎng)到2020 年的2030
億美元以上；中國(guó)報(bào)告大廳發(fā)布的大數(shù)據(jù)行業(yè)報(bào)告數(shù)據(jù)也說明，自2017 年起,我國(guó)大數(shù)據(jù)
產(chǎn)業(yè)將迎來發(fā)展黃金期，未來2~3 年的市場(chǎng)規(guī)模增長(zhǎng)率將保持在35%左右。
數(shù)據(jù)采集、數(shù)據(jù)存儲(chǔ)、數(shù)據(jù)挖掘、數(shù)據(jù)分析等大數(shù)據(jù)技術(shù)在越來越多的行業(yè)中得到
應(yīng)用，隨之而來的就是大數(shù)據(jù)人才問題的凸顯。麥肯錫預(yù)測(cè)，每年數(shù)據(jù)科學(xué)專業(yè)的應(yīng)屆
畢業(yè)生將增加7%，然而僅高質(zhì)量項(xiàng)目對(duì)于專業(yè)數(shù)據(jù)科學(xué)家的需求每年就會(huì)增加12%，完
全供不應(yīng)求。根據(jù)《人民日?qǐng)?bào)》的報(bào)道，未來3~5 年，中國(guó)需要180 萬數(shù)據(jù)人才，但目
前只有約30 萬人，人才缺口達(dá)到150 萬之多。
以貴州大學(xué)為例，其首屆大數(shù)據(jù)專業(yè)研究生就業(yè)率就達(dá)到100%，可以說“一搶而空”。
急切的人才需求直接催熱了大數(shù)據(jù)專業(yè)，國(guó)家教育部正式設(shè)立“數(shù)據(jù)科學(xué)與大數(shù)據(jù)技術(shù)”
本科新專業(yè)。目前已經(jīng)有兩批共計(jì)35 所大學(xué)獲批，包括北京大學(xué)、中南大學(xué)、對(duì)外經(jīng)
濟(jì)貿(mào)易大學(xué)、中國(guó)人民大學(xué)、北京郵電大學(xué)、復(fù)旦大學(xué)等。估計(jì)2018 年會(huì)有幾百所高
校獲批。
不過，就目前而言，在大數(shù)據(jù)人才培養(yǎng)和大數(shù)據(jù)課程建設(shè)方面，大部分高校仍然處
于起步階段，需要探索的還有很多。首先，大數(shù)據(jù)是個(gè)新生事物，懂大數(shù)據(jù)的老師少之總序
短短幾年間，大數(shù)據(jù)就以一日千里的發(fā)展速度，快速實(shí)現(xiàn)了從概念到落地，直接帶
動(dòng)了相關(guān)產(chǎn)業(yè)井噴式發(fā)展。全球多家研究機(jī)構(gòu)統(tǒng)計(jì)數(shù)據(jù)顯示，大數(shù)據(jù)產(chǎn)業(yè)將迎來發(fā)展黃
金期：IDC 預(yù)計(jì)，大數(shù)據(jù)和分析市場(chǎng)將從2016 年的1300 億美元增長(zhǎng)到2020 年的2030
億美元以上；中國(guó)報(bào)告大廳發(fā)布的大數(shù)據(jù)行業(yè)報(bào)告數(shù)據(jù)也說明，自2017 年起,我國(guó)大數(shù)據(jù)
產(chǎn)業(yè)將迎來發(fā)展黃金期，未來2~3 年的市場(chǎng)規(guī)模增長(zhǎng)率將保持在35%左右。
數(shù)據(jù)采集、數(shù)據(jù)存儲(chǔ)、數(shù)據(jù)挖掘、數(shù)據(jù)分析等大數(shù)據(jù)技術(shù)在越來越多的行業(yè)中得到
應(yīng)用，隨之而來的就是大數(shù)據(jù)人才問題的凸顯。麥肯錫預(yù)測(cè)，每年數(shù)據(jù)科學(xué)專業(yè)的應(yīng)屆
畢業(yè)生將增加7%，然而僅高質(zhì)量項(xiàng)目對(duì)于專業(yè)數(shù)據(jù)科學(xué)家的需求每年就會(huì)增加12%，完
全供不應(yīng)求。根據(jù)《人民日?qǐng)?bào)》的報(bào)道，未來3~5 年，中國(guó)需要180 萬數(shù)據(jù)人才，但目
前只有約30 萬人，人才缺口達(dá)到150 萬之多。
以貴州大學(xué)為例，其首屆大數(shù)據(jù)專業(yè)研究生就業(yè)率就達(dá)到100%，可以說“一搶而空”。
急切的人才需求直接催熱了大數(shù)據(jù)專業(yè)，國(guó)家教育部正式設(shè)立“數(shù)據(jù)科學(xué)與大數(shù)據(jù)技術(shù)”
本科新專業(yè)。目前已經(jīng)有兩批共計(jì)35 所大學(xué)獲批，包括北京大學(xué)、中南大學(xué)、對(duì)外經(jīng)
濟(jì)貿(mào)易大學(xué)、中國(guó)人民大學(xué)、北京郵電大學(xué)、復(fù)旦大學(xué)等。估計(jì)2018 年會(huì)有幾百所高
校獲批。
不過，就目前而言，在大數(shù)據(jù)人才培養(yǎng)和大數(shù)據(jù)課程建設(shè)方面，大部分高校仍然處
于起步階段，需要探索的還有很多。首先，大數(shù)據(jù)是個(gè)新生事物，懂大數(shù)據(jù)的老師少之
又少，院校缺“人”；其次，尚未形成完善的大數(shù)據(jù)人才培養(yǎng)和課程體系，院校缺“機(jī)制”；
再次，大數(shù)據(jù)實(shí)驗(yàn)需要為每位學(xué)生提供集群計(jì)算機(jī)，院校缺“機(jī)器”；*后，院校沒有海
量數(shù)據(jù)，開展大數(shù)據(jù)教學(xué)科研工作缺“原材料”。
其實(shí)，早在網(wǎng)格計(jì)算和云計(jì)算興起時(shí)，我國(guó)科技工作者就曾遇到過類似的挑戰(zhàn)，我
有幸參與了這些問題的解決過程。為了解決網(wǎng)格計(jì)算問題，我在清華大學(xué)讀博期間，于
2001 年創(chuàng)辦了中國(guó)網(wǎng)格信息中轉(zhuǎn)站網(wǎng)站，每天花幾個(gè)小時(shí)收集和分享有價(jià)值的資料給學(xué)
術(shù)界，此后我也多次籌辦和主持全國(guó)性的網(wǎng)格計(jì)算學(xué)術(shù)會(huì)議，進(jìn)行信息傳遞與知識(shí)分享。
2002 年，我與其他專家合作的《網(wǎng)格計(jì)算》教材也正式面世。
2008 年，當(dāng)云計(jì)算開始萌芽之時(shí)，我創(chuàng)辦了中國(guó)云計(jì)算網(wǎng)站（chinacloud.cn）（在各
大搜索引擎“云計(jì)算”關(guān)鍵詞中排名*），2010 年出版了《云計(jì)算（*版）》、2011
年出版了《云計(jì)算（第二版）》、2015 年出版了《云計(jì)算（第三版）》，每一版都花費(fèi)了大
量成本制作并免費(fèi)分享對(duì)應(yīng)的幾十個(gè)教學(xué)PPT。目前，這些PPT 的下載總量達(dá)到了幾百
萬次之多。同時(shí)，《云計(jì)算》教材也成為國(guó)內(nèi)高校的*教材，在CNKI 公布的高被引圖
書名單中，對(duì)于2010 年以來出版的所有圖書，《云計(jì)算(*版)》在自動(dòng)化和計(jì)算機(jī)領(lǐng)域
排名全國(guó)*。除了資料分享，在2010 年，我也在南京組織了全國(guó)高校云計(jì)算師資培訓(xùn)
班，培養(yǎng)了國(guó)內(nèi)*批云計(jì)算老師，并通過與華為、中興、360 等知名企業(yè)合作，輸出云
計(jì)算技術(shù)，培養(yǎng)云計(jì)算研發(fā)人才。這些工作獲得了大家的認(rèn)可與好評(píng)，此后我接連擔(dān)任
了工信部云計(jì)算研究中心專家、中國(guó)云計(jì)算專家委員會(huì)云存儲(chǔ)組組長(zhǎng)等職位。
近幾年，面對(duì)日益突出的大數(shù)據(jù)發(fā)展難題，我也正在嘗試使用此前類似的辦法去應(yīng)
對(duì)這些挑戰(zhàn)。為了解決大數(shù)據(jù)技術(shù)資料缺乏和交流不夠通透的問題，我于2013 年創(chuàng)辦了
中國(guó)大數(shù)據(jù)網(wǎng)站（thebigdata.cn），投入大量的人力進(jìn)行日常維護(hù)，該網(wǎng)站目前已經(jīng)在各
大搜索引擎的“大數(shù)據(jù)”關(guān)鍵詞排名中位居*；為了解決大數(shù)據(jù)師資匱乏的問題，我
面向全國(guó)院校陸續(xù)舉辦多期大數(shù)據(jù)師資培訓(xùn)班。2016 年末至今，在南京多次舉辦全國(guó)高
校/高職/中職大數(shù)據(jù)免費(fèi)培訓(xùn)班，基于《大數(shù)據(jù)》《大數(shù)據(jù)實(shí)驗(yàn)手冊(cè)》以及云創(chuàng)大數(shù)據(jù)提
供的大數(shù)據(jù)實(shí)驗(yàn)平臺(tái)，幫助到場(chǎng)老師們跑通了Hadoop、Spark 等多個(gè)大數(shù)據(jù)實(shí)驗(yàn)，使他
們跨過了“從理論到實(shí)踐，從知道到用過”的門檻。2017 年5 月，還舉辦了全國(guó)千所高
校大數(shù)據(jù)師資免費(fèi)講習(xí)班，盛況空前。
其中，為了解決大數(shù)據(jù)實(shí)驗(yàn)難的問題而開發(fā)的大數(shù)據(jù)實(shí)驗(yàn)平臺(tái)，正在為越來越多高
校的教學(xué)科研帶去方便：2016 年，我?guī)ьI(lǐng)云創(chuàng)大數(shù)據(jù)（www.cstor.cn，股票代碼：835305）
的科研人員，應(yīng)用Docker 容器技術(shù)，成功開發(fā)了BDRack 大數(shù)據(jù)實(shí)驗(yàn)一體機(jī)，它打破虛
擬化技術(shù)的性能瓶頸，可以為每一位參加實(shí)驗(yàn)的人員虛擬出Hadoop 集群、Spark 集群、
Storm 集群等，自帶實(shí)驗(yàn)所需數(shù)據(jù)，并準(zhǔn)備了詳細(xì)的實(shí)驗(yàn)手冊(cè)（包含42 個(gè)大數(shù)據(jù)實(shí)驗(yàn)）、
PPT 和實(shí)驗(yàn)過程視頻，可以開展大數(shù)據(jù)管理、大數(shù)據(jù)挖掘等各類實(shí)驗(yàn)，并可進(jìn)行精確營(yíng)
銷、信用分析等多種實(shí)戰(zhàn)演練。目前，大數(shù)據(jù)實(shí)驗(yàn)平臺(tái)已經(jīng)在鄭州大學(xué)、西京學(xué)院、鄭
州升達(dá)經(jīng)貿(mào)管理學(xué)院、鎮(zhèn)江高等職業(yè)技術(shù)學(xué)校等多所院校成功應(yīng)用，并廣受校方好評(píng)。
該平臺(tái)也以云服務(wù)的方式在線提供（大數(shù)據(jù)實(shí)驗(yàn)平臺(tái)，https://bd.cstor.cn），幫助師生通過
自學(xué)，用一個(gè)月左右成為大數(shù)據(jù)動(dòng)手的高手。
同時(shí)，為了解決缺乏權(quán)威大數(shù)據(jù)教材的問題，我所負(fù)責(zé)的南京大數(shù)據(jù)研究院，聯(lián)合
金陵科技學(xué)院、河南大學(xué)、云創(chuàng)大數(shù)據(jù)、中國(guó)地震局等多家單位，歷時(shí)兩年，編著出版
了適合本科教學(xué)的《大數(shù)據(jù)》《大數(shù)據(jù)庫》《大數(shù)據(jù)實(shí)驗(yàn)手冊(cè)》等教材。另外，《數(shù)據(jù)挖掘》
《虛擬化與容器》《大數(shù)據(jù)可視化》《深度學(xué)習(xí)》等本科教材也將于近期出版。在大數(shù)據(jù)教
學(xué)中，本科院校的實(shí)踐教學(xué)應(yīng)更加系統(tǒng)性，偏向新技術(shù)的應(yīng)用，且對(duì)工程實(shí)踐能力要求
更高。而高職、高專院校則更偏向于技術(shù)性和技能訓(xùn)練，理論以夠用為主，學(xué)生將主要
從事數(shù)據(jù)清洗和運(yùn)維方面的工作。基于此，我們還聯(lián)合多家高職院校專家準(zhǔn)備了《云計(jì)
算基礎(chǔ)》《大數(shù)據(jù)基礎(chǔ)》《數(shù)據(jù)挖掘基礎(chǔ)》《R 語言》《數(shù)據(jù)清洗》《大數(shù)據(jù)系統(tǒng)運(yùn)維》《大
數(shù)據(jù)實(shí)踐》系列教材，目前也已經(jīng)陸續(xù)進(jìn)入定稿出版階段。
此外，我們也將繼續(xù)在中國(guó)大數(shù)據(jù)（thebigdata.cn）和中國(guó)云計(jì)算（chinacloud.cn）
等網(wǎng)站免費(fèi)提供配套PPT 和其他資料。同時(shí)， 持續(xù)開放大數(shù)據(jù)實(shí)驗(yàn)平臺(tái)
（https://bd.cstor.cn）、免費(fèi)的物聯(lián)網(wǎng)大數(shù)據(jù)托管平臺(tái)萬物云（wanwuyun.com）和環(huán)境大數(shù)
據(jù)免費(fèi)分享平臺(tái)環(huán)境云（envicloud.cn），使資源與數(shù)據(jù)隨手可得，讓大數(shù)據(jù)學(xué)習(xí)變得更加
輕松。
在此，特別感謝我的碩士導(dǎo)師謝希仁教授和博士導(dǎo)師李三立院士。謝希仁教授所著
的《計(jì)算機(jī)網(wǎng)絡(luò)》已經(jīng)更新到第7 版，與時(shí)俱進(jìn)且日臻完美，時(shí)時(shí)提醒學(xué)生要以這樣的
標(biāo)準(zhǔn)來寫書。李三立院士是留蘇博士，為我國(guó)計(jì)算機(jī)事業(yè)做出了杰出貢獻(xiàn)，曾任國(guó)家攀
登計(jì)劃項(xiàng)目首席科學(xué)家。他的嚴(yán)謹(jǐn)治學(xué)帶出了一大批杰出的學(xué)生。
本叢書是集體智慧的結(jié)晶，在此謹(jǐn)向付出辛勤勞動(dòng)的各位作者致敬！書中難免會(huì)有
不當(dāng)之處，請(qǐng)讀者不吝賜教。我的郵箱：gloud@126.com，微信公眾號(hào)：劉鵬看未來
（lpoutlook）。
劉鵬 教授
于南京大數(shù)據(jù)研究院
前言
21 世紀(jì)初，人類邁入大數(shù)據(jù)時(shí)代，各行各業(yè)擁抱大數(shù)據(jù)，希冀借大數(shù)據(jù)挖掘與分
析來促進(jìn)產(chǎn)業(yè)升級(jí)與變革。因此，大數(shù)據(jù)人才的需求呈現(xiàn)井噴之勢(shì)。
中國(guó)云計(jì)算專家咨詢委員會(huì)秘書長(zhǎng)劉鵬教授順勢(shì)而為，周密思考，提出高級(jí)大數(shù)據(jù)
人才培養(yǎng)課程體系，并邀請(qǐng)全國(guó)上百家高校中從事一線教學(xué)科研任務(wù)的教師一起，編撰
高級(jí)大數(shù)據(jù)人才培養(yǎng)叢書。本書即該套叢書之一。
本書的定位是大數(shù)據(jù)挖掘技術(shù)與應(yīng)用。以“讓學(xué)習(xí)變得輕松”為根本出發(fā)點(diǎn)，本書
努力回答：數(shù)據(jù)挖掘是什么？發(fā)展如何？經(jīng)典的數(shù)據(jù)挖掘算法有哪些？大數(shù)據(jù)環(huán)境下數(shù)
據(jù)挖掘有哪些新特點(diǎn)和新延展？如何分析實(shí)際問題，如何應(yīng)用？本書編寫的指導(dǎo)思想有
三：一是理論與應(yīng)用相呼應(yīng)。從數(shù)據(jù)挖掘算法理論與方法、工具和應(yīng)用兩方面進(jìn)行闡述，
既注重理論，同時(shí)貼近實(shí)戰(zhàn)，解行結(jié)合，希望學(xué)習(xí)者既能很快將理論應(yīng)用于實(shí)際領(lǐng)域的
數(shù)據(jù)分析中，同時(shí)也具備厚積薄發(fā)的能力；二是基礎(chǔ)與發(fā)展一脈相承。大數(shù)據(jù)新常態(tài)下
經(jīng)典數(shù)據(jù)挖掘的基本原理仍然適用，不同之處在于，根據(jù)現(xiàn)有分布式、并行環(huán)境，對(duì)原
有算法進(jìn)行優(yōu)化。本書擬循序漸進(jìn)地介紹經(jīng)典數(shù)據(jù)挖掘算法，以及大數(shù)據(jù)環(huán)境下數(shù)據(jù)挖
掘算法的新特點(diǎn)和新延展，有助于學(xué)習(xí)者全面掌握數(shù)據(jù)挖掘理論；三是局部與全局整體
聯(lián)動(dòng)。本書屬于高級(jí)大數(shù)據(jù)人才培養(yǎng)叢書系列教材，因此，在本書內(nèi)容組織上，需要考
慮與叢書其他教材的關(guān)系，既緊密聯(lián)系又自成一體，共同組成高級(jí)大數(shù)據(jù)人才培養(yǎng)課程
體系。
基于上述指導(dǎo)思想，本書內(nèi)容分為四部分：一是概念與基礎(chǔ)，見第1 章緒論和第2
章；二是經(jīng)典的數(shù)據(jù)挖掘算法，見第3 章分類、第4 章回歸、第5 章聚類和第6 章關(guān)聯(lián)
規(guī)則；三是大數(shù)據(jù)挖掘技術(shù)，其中，第7 章重點(diǎn)介紹了大數(shù)據(jù)環(huán)境下經(jīng)典數(shù)據(jù)挖掘算法
的優(yōu)化與改進(jìn)，第8 章介紹了推薦系統(tǒng)的理論與方法，第9 章則對(duì)鏈接分析與網(wǎng)頁排序、
互聯(lián)網(wǎng)信息抽取、日志挖掘與查詢分析等技術(shù)進(jìn)行了介紹；四是常用數(shù)據(jù)挖掘工具（包），
見附錄。
本書成稿過程中得到叢書主編劉鵬教授和叢書副主編金陵科技學(xué)院張燕副院長(zhǎng)的大
力支持，在書稿提綱和內(nèi)容組織上提出了諸多建設(shè)性意見。同時(shí)，兩輪審稿評(píng)審專家對(duì)
本書給予了全面指導(dǎo)和幫助，在此一并致謝。
當(dāng)前，大數(shù)據(jù)挖掘技術(shù)仍處在高速發(fā)展的歷史階段，其概念內(nèi)涵、技術(shù)方法、應(yīng)用
模式還在不斷創(chuàng)新演化之中，由于時(shí)間和水平所限，本書還存在缺點(diǎn)和不足，歡迎大家
不吝賜教。
數(shù)據(jù)挖掘 目錄
第1 章 緒論 ··························································································································1
1.1 數(shù)據(jù)挖掘基本概念 ··································································································1
1.1.1 數(shù)據(jù)挖掘的概念 ··························································································1
1.1.2 大數(shù)據(jù)環(huán)境下的數(shù)據(jù)挖掘 ···········································································2
1.1.3 數(shù)據(jù)挖掘的特性 ··························································································3
1.1.4 數(shù)據(jù)挖掘的過程 ··························································································3
1.2 數(shù)據(jù)挖掘起源及發(fā)展歷史 ······················································································4
1.3 數(shù)據(jù)挖掘常用工具 ··································································································7
1.3.1 商用工具 ······································································································7
1.3.2 開源工具 ······································································································8
1.4 數(shù)據(jù)挖掘應(yīng)用場(chǎng)景 ································································································ 10
習(xí)題 ································································································································ 12
參考文獻(xiàn) ························································································································ 13
第2 章 數(shù)據(jù)預(yù)處理與相似性 ····························································································· 14
2.1 數(shù)據(jù)類型 ··············································································································· 14
2.1.1 屬性與度量 ································································································ 14
2.1.2 數(shù)據(jù)集的類型 ···························································································· 15
2.2 數(shù)據(jù)預(yù)處理 ··········································································································· 16
2.2.1 數(shù)據(jù)清理 ···································································································· 16
2.2.2 數(shù)據(jù)集成 ···································································································· 18
2.2.3 數(shù)據(jù)規(guī)范化 ································································································ 19
2.2.4 數(shù)據(jù)約簡(jiǎn) ···································································································· 20
2.2.5 數(shù)據(jù)離散化 ································································································ 22
2.3 數(shù)據(jù)的相似性 ······································································································· 23
2.3.1 數(shù)值屬性的相似性度量 ············································································· 23
2.3.2 標(biāo)稱屬性的相似性度量 ············································································· 26
2.3.3 組合異種屬性的相似性度量 ····································································· 27
2.3.4 文檔相似性度量 ························································································ 28
2.3.5 離散序列相似性度量 ················································································· 30
習(xí)題 ································································································································ 31
參考文獻(xiàn) ························································································································ 32
第3 章 分類 ························································································································ 33
3.1 分類的基本概念、分類過程及分類器性能的評(píng)估 ············································· 33
3.1.1 分類的基本概念 ························································································ 33
3.1.2 分類的過程 ································································································ 33
3.1.3 分類器性能的評(píng)估方法 ············································································· 34
3.2 決策樹 ···········································································
1.1 數(shù)據(jù)挖掘基本概念 ··································································································1
1.1.1 數(shù)據(jù)挖掘的概念 ··························································································1
1.1.2 大數(shù)據(jù)環(huán)境下的數(shù)據(jù)挖掘 ···········································································2
1.1.3 數(shù)據(jù)挖掘的特性 ··························································································3
1.1.4 數(shù)據(jù)挖掘的過程 ··························································································3
1.2 數(shù)據(jù)挖掘起源及發(fā)展歷史 ······················································································4
1.3 數(shù)據(jù)挖掘常用工具 ··································································································7
1.3.1 商用工具 ······································································································7
1.3.2 開源工具 ······································································································8
1.4 數(shù)據(jù)挖掘應(yīng)用場(chǎng)景 ································································································ 10
習(xí)題 ································································································································ 12
參考文獻(xiàn) ························································································································ 13
第2 章 數(shù)據(jù)預(yù)處理與相似性 ····························································································· 14
2.1 數(shù)據(jù)類型 ··············································································································· 14
2.1.1 屬性與度量 ································································································ 14
2.1.2 數(shù)據(jù)集的類型 ···························································································· 15
2.2 數(shù)據(jù)預(yù)處理 ··········································································································· 16
2.2.1 數(shù)據(jù)清理 ···································································································· 16
2.2.2 數(shù)據(jù)集成 ···································································································· 18
2.2.3 數(shù)據(jù)規(guī)范化 ································································································ 19
2.2.4 數(shù)據(jù)約簡(jiǎn) ···································································································· 20
2.2.5 數(shù)據(jù)離散化 ································································································ 22
2.3 數(shù)據(jù)的相似性 ······································································································· 23
2.3.1 數(shù)值屬性的相似性度量 ············································································· 23
2.3.2 標(biāo)稱屬性的相似性度量 ············································································· 26
2.3.3 組合異種屬性的相似性度量 ····································································· 27
2.3.4 文檔相似性度量 ························································································ 28
2.3.5 離散序列相似性度量 ················································································· 30
習(xí)題 ································································································································ 31
參考文獻(xiàn) ························································································································ 32
第3 章 分類 ························································································································ 33
3.1 分類的基本概念、分類過程及分類器性能的評(píng)估 ············································· 33
3.1.1 分類的基本概念 ························································································ 33
3.1.2 分類的過程 ································································································ 33
3.1.3 分類器性能的評(píng)估方法 ············································································· 34
3.2 決策樹 ···········································································
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數(shù)據(jù)挖掘 作者簡(jiǎn)介
1995年在重慶大學(xué)獲得學(xué)士學(xué)位，2003年在中國(guó)人民解放軍后勤工程學(xué)院獲得碩士學(xué)位，2012年在清華大學(xué)獲得博士學(xué)位，現(xiàn)于中國(guó)人民解放軍陸軍勤務(wù)學(xué)院任教，副教授、碩導(dǎo)。計(jì)算機(jī)科學(xué)與技術(shù)學(xué)科、后勤信息化學(xué)科中青年骨干。主（持）研科研項(xiàng)目35項(xiàng)，其中，主持國(guó)家級(jí)科研項(xiàng)目1項(xiàng)、軍隊(duì)（省部級(jí)）6項(xiàng)。發(fā)表論文40余篇，SCI收錄期刊論文2篇（1作）、EI收錄論文10篇（1作6篇）。出版專著3本，獲國(guó)家發(fā)明專利3項(xiàng)。獲教育部科技進(jìn)步一等獎(jiǎng)1項(xiàng)，多次榮獲學(xué)院優(yōu)秀教員榮譽(yù)稱號(hào)、學(xué)院教學(xué)研究成果獎(jiǎng)和教學(xué)質(zhì)量獎(jiǎng)。
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