數(shù)據(jù)挖掘5學習教案

1、會計學1數(shù)據(jù)挖掘數(shù)據(jù)挖掘5第一頁，共41頁。2022-4-192第1頁/共41頁第二頁，共41頁。2022-4-193approach第2頁/共41頁第三頁，共41頁。2022-4-194第3頁/共41頁第四頁，共41頁。2022-4-195第4頁/共41頁第五頁，共41頁。2022-4-196第5頁/共41頁第六頁，共41頁。2022-4-197第6頁/共41頁第七頁，共41頁。2022-4-198第7頁/共41頁第八頁，共41頁。2022-4-199 Prime generalized relation第8頁/共41頁第九頁，共41頁。2022-4-1910第9頁/共41頁第十頁，共41頁

2、。2022-4-1911第10頁/共41頁第十一頁，共41頁。2022-4-1912第11頁/共41頁第十二頁，共41頁。2022-4-1913sales in 1997第12頁/共41頁第十三頁，共41頁。2022-4-1914sales in 1997第13頁/共41頁第十四頁，共41頁。2022-4-1915sales in 1997第14頁/共41頁第十五頁，共41頁。2022-4-1916sales in 1997第15頁/共41頁第十六頁，共41頁。2022-4-1917第16頁/共41頁第十七頁，共41頁。2022-4-1918 X, item(X)=“computer”(loc

3、ation(X)=“Asia”)t:25.00% location(X)=“Europe”)t:30.00% (location(X)=“North_America”)t:45.00%a logic rule that is associated with quantitative information is called a quantitative rulethe general form of a quantitative characteristic rule is: X, target_class(X)condition1(X)t:w1conditionn(X)t:wnwhere

4、t-weight describes the typicality of each disjunct in the rulecharacteristic rule is necessary condition of the target class Nttnqcountqcountweightt1)()(_第17頁/共41頁第十八頁，共41頁。2022-4-1919第18頁/共41頁第十九頁，共41頁。2022-4-1920第19頁/共41頁第二十頁，共41頁。2022-4-1921第20頁/共41頁第二十一頁，共41頁。2022-4-1922SSSSSSSIimiim2121log),( s

5、uppose attribute A is selected to partition the training set into the subsets S1, S2, , Sv, then the entropy of A, i.e. the information needed to classify all the instances in those subsets is mijijjijvjjSSSSSSAEnt121log)(where Sij is the instances of class Ci that are covered by Sjthen the informat

6、ion gain of selecting A isthe bigger the information gain, the more relevant the attribute A)(),()(21AEntSSSIAGainm 第21頁/共41頁第二十二頁，共41頁。2022-4-1923Contrasting class: Undergraduate students (=130)gendermajorbirth_countryage_rangegpacountMFMFMFScience Science Engineering Science Science Engineering Ca

7、nada Foreign Foreign Foreign Canada Canada 20-25 25-30 25-30 25-30 20-25 20-25 Very_good ExcellentExcellent Excellent Excellent Excellent 16221825 2118gendermajorbirth_countryage_rangegpacountMFMFMFScience Business Business Science Engineering Engineering Foreign Canada Canada Canada Foreign Canada2

8、0 20 2020-2520-2520 Very_goodFairFairFair Very_good Excellent 18202224 2224第22頁/共41頁第二十三頁，共41頁。2022-4-192416221825 2118countGSGSGSGSGSGSVery_good ExcellentExcellent Excellent Excellent Excellent 20-25 25-30 25-30 25-30 20-25 20-25 Canada Foreign Foreign Foreign Canada Canada Science Science Engineer

9、ing Science Science Engineering MFMFMFclassgpaage_rangebirth_countrymajorgender18202224 2224SSSSSSVery_goodFairFairFair Very_good Excellent 20 20 2020-2520-2520 Foreign Canada Canada Canada Foreign CanadaScience Business Business Science Engineering Engineering MFMFMF第23頁/共41頁第二十四頁，共41頁。2022-4-19259

10、988. 0250130log250130250120log250120)130,120(),(2221 ISSI suppose attribute major is selected to partition the training setfor major = “Science”: S11 = 84, S21 = 429183. 012642log1264212684log12684),(222111 SSIfor major = “Engineer”: S12 = 36, S22 = 469892. 08246log82468236log8236),(222212 SSIfor ma

11、jor = “Engineer”: S13 = 0, S23 = 420),(2313 SSI then the entropy of major is7873. 0),(25042),(25082),(250126)(231322122111 SSISSISSImajorEnt第24頁/共41頁第二十五頁，共41頁。2022-4-1926 now suppose we use an attribute relevance threshold of 0.1:gender and birth_country are removed as weakly relevant attributesmaj

12、or, gpa, and age_range are kept as strong relevant attributes2115. 0)(),()(21 majorEntSSImajorGain we can also get the information gain of other attributes:5971. 0)_(4490. 0)(0407. 0)_(0003. 0)( rabgeageGaingpaGaincountrybirthGaingenderGain第25頁/共41頁第二十六頁，共41頁。2022-4-1927第26頁/共41頁第二十七頁，共41頁。2022-4-19

13、28第27頁/共41頁第二十八頁，共41頁。2022-4-1929Prime generalized relation for the contrasting class: Undergraduate studentsbirth_countryage_rangegpacount %CanadaCanadaCanadaOther20-2525-30Over_30 Over_30 Good Good Very_good Excellent 5.532.32 5.86 4.68 birth_countryage_rangegpacount %CanadaCanadaCanadaOther15-201

14、5-20 25-30 Over_30 Fair Good Good Excellent5.53 4.535.02 0.68第28頁/共41頁第二十九頁，共41頁。2022-4-1930 NiiajaCqcountCqcountweightd1)()(_discriminant rule is sufficient condition of the target classexample:statusbirth_countryage_rangegpacountGraduateCanada25-30Good90UndergraduateCanada 25-30 Good210 X,graduate

15、_student(X) birth_country(X)=“Canada” age_range(X)=“25-30” gpa(X)=“good”d:30%第29頁/共41頁第三十頁，共41頁。2022-4-1931第30頁/共41頁第三十一頁，共41頁。2022-4-1932 X, target_class(X) condtion1(X)t:w1, d:w1condtionn(X)t:wn, d:wn X, Europe(X) (item(X)=“TV”)t:25%, d:40%(item(X)=“computer”)t:75%,d:30第31頁/共41頁第三十二頁，共41頁。2022-4-1

16、933measures for central tendency:meanmedianmodemidrangemeasures for dispersion:quartilesvariancestandard deviation第32頁/共41頁第三十三頁，共41頁。2022-4-1934 Mean weighted arithmetic mean (algebraic) niixnx11 niiniiiwxwx11第33頁/共41頁第三十四頁，共41頁。2022-4-1935 (algebraic) standard deviation the square root of s2 (algebraic) nininiiiixnxnxxns1121222111)(11第34頁/共41頁第三十五頁，共41頁。2022-4-1936