數(shù)據(jù)挖掘：概念、模型、方法和算法

                    
                    
 第1章 數(shù)據(jù)挖掘的概念                  
                   
 1.1 概述                  
 1.2 數(shù)據(jù)挖掘的起源                  
 1.3 數(shù)據(jù)挖掘過程                  
 1.3.1 陳述問題和闡明假設(shè)                  
 1.3.2 數(shù)據(jù)收集                  
 1.3.3 數(shù)據(jù)預(yù)處理                  
 1.3.4 模型評估                  
 1.3.5 解釋模型和得出結(jié)論                  
 1.4 大型數(shù)據(jù)集                  
 1.5 數(shù)據(jù)倉庫                  
 1.6 本書的結(jié)構(gòu)                  
 1.7 復(fù)習(xí)題                  
 1.8 參考書目                  
                   
 第2章 數(shù)據(jù)準(zhǔn)備                  
                   
 2.1 原始數(shù)據(jù)的表述                  
 2.2 原始數(shù)據(jù)的特性                  
 2.3 原始數(shù)據(jù)的轉(zhuǎn)換                  
 2.4 丟失數(shù)據(jù)                  
 2.5 時間相關(guān)數(shù)據(jù)                  
 2.6 異常點分析                  
 2.7 復(fù)習(xí)題                  
 2.8 參考書目                  
                   
 第3章 數(shù)據(jù)歸約                  
                   
 3.1 大型數(shù)據(jù)集的維度                  
 3.2 特征歸約                  
 3.3 特征排列的熵度量                  
 3.4 主成分分析                  
 3.5 值歸約                  
 3.6 特征離散化：ChiMerge技術(shù)                  
 3.7 案例歸約                  
 3.8 復(fù)習(xí)題                  
 3.9 參考書目                  
                   
 第4章 從數(shù)據(jù)中學(xué)習(xí)                  
                   
 4.1 機(jī)器學(xué)習(xí)                  
 4.2 統(tǒng)計學(xué)習(xí)原理                  
 4.3 學(xué)習(xí)方法的類型                  
 4.4 常見的學(xué)習(xí)任務(wù)                  
 4.5 模型估計                  
 4.6 復(fù)習(xí)題                  
 4.7 參考書目                  
                   
 第5章 統(tǒng)計方法                  
                   
 5.1 統(tǒng)計推斷                  
 5.2 評測數(shù)據(jù)集的差異                  
 5.3 貝葉斯定理                  
 5.4 預(yù)測回歸                  
 5.5 方差分析                  
 5.6 對數(shù)回歸                  
 5.7 對數(shù)-線性模型                  
 5.8 線性判別分析                  
 5.9 復(fù)習(xí)題                  
 5.10 參考書目                  
                   
 第6章 聚類分析                  
                   
 6.1 聚類概念                  
 6.2 相似度的度量                  
 6.3 凝聚層次的聚類                  
 6.4 分區(qū)聚類                  
 6.5 增量聚類                  
 6.6 復(fù)習(xí)題                  
 6.7 參考書目                  
                   
 第7章 決策樹和決策規(guī)則                  
                   
 7.1 決策樹                  
 7.2 C4.5算法：生成一個決策樹                  
 7.3 未知屬性值                  
 7.4 修剪決策樹                  
 7.5 C4.5算法：生成決策規(guī)則                  
 7.6 決策樹和決策規(guī)則的局限性                  
 7.7 關(guān)聯(lián)分類方法                  
 7.8 復(fù)習(xí)題                  
 7.9 參考書目                  
                   
 第8章 關(guān)聯(lián)規(guī)則                  
                   
 8.1 購物籃分析                  
 8.2 APRIORI算法                  
 8.3 從頻繁項集得到關(guān)聯(lián)規(guī)則                  
 8.4 提高APRIORI算法的效率                  
 8.5 頻繁模式增長方法（FP-增長方法）                  
 8.6 多維關(guān)聯(lián)規(guī)則挖掘                  
 8.7 WEB挖掘                  
 8.8 HITS和LOGSOM算法                  
 8.9 挖掘路徑遍歷模式                  
 8.10 文本挖掘                  
 8.11 復(fù)習(xí)題                  
 8.12 參考書目                  
                   
 第9章 人式神經(jīng)網(wǎng)絡(luò)                  
                   
 9.1 人工神經(jīng)元的模型                  
 9.2 人工神經(jīng)網(wǎng)絡(luò)的結(jié)構(gòu)                  
 9.3 學(xué)習(xí)過程                  
 9.4 學(xué)習(xí)任務(wù)                  
 9.5 多層感知機(jī)                  
 9.6 競爭網(wǎng)絡(luò)和競爭學(xué)習(xí)                  
 9.7 復(fù)習(xí)題                  
 9.8 參考書目                  
                   
 第10章 遺傳算法                  
                   
 10.1 遺傳算法的基本原理                  
 10.2 用遺傳算法進(jìn)行優(yōu)化                  
 10.3 遺傳算法的一個簡單例證                  
 10.4 圖式（SCHEMATA）                  
 10.5 旅行推銷員問題                  
 10.6 使用遺傳算法的機(jī)器學(xué)習(xí)                  
 10.7 復(fù)習(xí)題                  
 10.8 參考書目                  
                   
 第11章 模糊集和模糊邏輯                  
                   
 11.1 模糊集                  
 11.2 模糊集的運(yùn)算                  
 11.3 擴(kuò)展原理和模糊關(guān)系                  
 11.4 模糊邏輯和模糊推理系統(tǒng)                  
 11.5 多因子評價                  
 11.6 從數(shù)據(jù)中提取模糊模型                  
 11.7 復(fù)習(xí)題                  
 11.8 參考書目                  
                   
 第12章 可視化方法                  
                   
 12.1 感知和可視化                  
 12.2 科學(xué)可視化和信息可視化                  
 12.3 平行坐標(biāo)                  
 12.4 放射性可視化                  
 12.5 KOHONEN自組織映射                  
 12.6 數(shù)據(jù)挖掘的可視化系統(tǒng)                  
 12.7 復(fù)習(xí)題                  
 12.8 參考書目                  
                   
 第13章 參考書目                  
                   
 附錄A 數(shù)據(jù)挖掘工具                  
                   
 附錄B 數(shù)據(jù)挖掘應(yīng)用