04-05-43ep3.nuwm.edu.ua/12130/1/04-05-43 (1).pdf · 2018. 11. 9. · 04-05-43 J H ; H J H = J : F...
Transcript of 04-05-43ep3.nuwm.edu.ua/12130/1/04-05-43 (1).pdf · 2018. 11. 9. · 04-05-43 J H ; H J H = J : F...
![Page 1: 04-05-43ep3.nuwm.edu.ua/12130/1/04-05-43 (1).pdf · 2018. 11. 9. · 04-05-43 J H ; H J H = J : F Ь H 2 И K PИ I E 1 GИ Program of the Discipline І g l _ e _ d l m Z eь b c](https://reader036.fdocuments.ec/reader036/viewer/2022062510/611bc9ceb3cd2d1ba369dd90/html5/thumbnails/1.jpg)
- ,
’
" Ю" - ,
______________ . .
"____" _______________ 2018 .
04-05-43
Ь И И И
Program of the Discipline
І ь Intelligent Data Analysis
c
specialty
122 “ ’ ”
122 “Computer sciences”
specialization
– 2018
![Page 2: 04-05-43ep3.nuwm.edu.ua/12130/1/04-05-43 (1).pdf · 2018. 11. 9. · 04-05-43 J H ; H J H = J : F Ь H 2 И K PИ I E 1 GИ Program of the Discipline І g l _ e _ d l m Z eь b c](https://reader036.fdocuments.ec/reader036/viewer/2022062510/611bc9ceb3cd2d1ba369dd90/html5/thumbnails/2.jpg)
2
“І ” ( ) 122
“ ’ ” / . . – : , 2018. – 15 .
: . ., . . , ’ .
’
“_30_” ____08____ 2018 № _1_
’ . .
- є 122 “ ’ ”.
“_30_” _____08____ 2018 № _1_
- І. .
© . . , 2018
© , 2018
![Page 3: 04-05-43ep3.nuwm.edu.ua/12130/1/04-05-43 (1).pdf · 2018. 11. 9. · 04-05-43 J H ; H J H = J : F Ь H 2 И K PИ I E 1 GИ Program of the Discipline І g l _ e _ d l m Z eь b c](https://reader036.fdocuments.ec/reader036/viewer/2022062510/611bc9ceb3cd2d1ba369dd90/html5/thumbnails/3.jpg)
3
“І ” - ,
( ) 122 “ ’ ”.
є , “ ”, “ ”.
“ ”, “ ”,
, .
“І ”
є ’є
. , ’ є
. є
. є
’ , є є ,
є .
К : , « », , , - , ,
, .
bstract The knowledge and skills acquired during the study of the discipline
"Intelligent Data Analysis" are integral components of the formation of
professional competence and an important aspect of academic and professional
training of students. The course program is designed for students, for whom the use
of computer technology in professional activities is a prerequisite for professional
success. The discipline program involves a comprehensive study of the main
aspects of the methods and models of data classification in the framework of a
competent approaches.
The course of the intellectual data analysis includes the main aspects of the
implementation of algorithms solutions to the problems of processing large
amounts of information, is one of the basic disciplines of professional training of
students, and it is based on the use of modern learning technologies.
Key words: clusterization, method of "nearest neighbor", precedence
considerations, data visualization, cross-tabulation, trust networks, neural
networks, genetic algorithms.
![Page 4: 04-05-43ep3.nuwm.edu.ua/12130/1/04-05-43 (1).pdf · 2018. 11. 9. · 04-05-43 J H ; H J H = J : F Ь H 2 И K PИ I E 1 GИ Program of the Discipline І g l _ e _ d l m Z eь b c](https://reader036.fdocuments.ec/reader036/viewer/2022062510/611bc9ceb3cd2d1ba369dd90/html5/thumbnails/4.jpg)
4
ь
, ,
–
3,5
12 “І ”
– 2
122 “ ’
”
:
– 2 2
І - : є
– 105
3
-
: – 4 .
– 6 .
- :
– 1 . – 9 .
( )
18 . 2 . ,
- -
18 . 8 .
69 . 95 . І : -
:
.
:
– 34,29% 65,71%.
– 9,52% 90,48%.
![Page 5: 04-05-43ep3.nuwm.edu.ua/12130/1/04-05-43 (1).pdf · 2018. 11. 9. · 04-05-43 J H ; H J H = J : F Ь H 2 И K PИ I E 1 GИ Program of the Discipline І g l _ e _ d l m Z eь b c](https://reader036.fdocuments.ec/reader036/viewer/2022062510/611bc9ceb3cd2d1ba369dd90/html5/thumbnails/5.jpg)
5
1. ь
“І ” є -
“ ’
” 122 “І ”. - .
є .
: , , .
“І ” є
. -
: :
– ; – ; OLAP DataMining;
:
– , , ,
; – ,
; :
– ; – ,
; ; – , .
2. ь
ь 1
1. я я
, Data Mining. . . . . . .
.
![Page 6: 04-05-43ep3.nuwm.edu.ua/12130/1/04-05-43 (1).pdf · 2018. 11. 9. · 04-05-43 J H ; H J H = J : F Ь H 2 И K PИ I E 1 GИ Program of the Discipline І g l _ e _ d l m Z eь b c](https://reader036.fdocuments.ec/reader036/viewer/2022062510/611bc9ceb3cd2d1ba369dd90/html5/thumbnails/6.jpg)
6
2. я . .
. . . , Support Vector Machine (SVM). .
є .
3. ь
. . . .
4. ь . .
OLAP- . . FASMI.
ь 2
5. . Іє .
6. . .
. . Apriori. .
7. .
. . . . . . .
.
8. OLAP-
OLAP- . MOLAP, ROLAP, HOLAP.
9. . я . я я я ь
. . І :
SAS Enterprise Miner, Poly Analyst, Cognos, STATISICA Data Miner, Oracle Data
Mining, Deductor, KXEN.
![Page 7: 04-05-43ep3.nuwm.edu.ua/12130/1/04-05-43 (1).pdf · 2018. 11. 9. · 04-05-43 J H ; H J H = J : F Ь H 2 И K PИ I E 1 GИ Program of the Discipline І g l _ e _ d l m Z eь b c](https://reader036.fdocuments.ec/reader036/viewer/2022062510/611bc9ceb3cd2d1ba369dd90/html5/thumbnails/7.jpg)
7
3. ь
.
.
. .
.
.
.
. .
.
1 2 3 4 5 6 7 8 9 10 11 12 13
ь 1
1.
-
11 2 - 2 - 7 12 1 - 1 - 10
2.
11 2 - 2 - 7 12 1 - 1 - 10
3.
ь
11 2 - 2 - 7 11 - - 1 - 10
4.
-
-
-
ь
11 2 - 2 - 7 11 - - 1 - 10
1
44 8 0 8 0 28 46 2 0 4 0 40
ь 2
5.
12 2 - 2 - 8 12 - - 1 - 11
![Page 8: 04-05-43ep3.nuwm.edu.ua/12130/1/04-05-43 (1).pdf · 2018. 11. 9. · 04-05-43 J H ; H J H = J : F Ь H 2 И K PИ I E 1 GИ Program of the Discipline І g l _ e _ d l m Z eь b c](https://reader036.fdocuments.ec/reader036/viewer/2022062510/611bc9ceb3cd2d1ba369dd90/html5/thumbnails/8.jpg)
8
6.
-
12 2 - 2 - 8 12 - - 1 - 11
7.
-
. -
12 2 - 2 - 8 12 - - 1 - 11
8.
А
OLAP-
12 2 - 2 - 8 12 - - 1 - 11
9.
-
. -
.
І
-
ь
13 2 - 2 - 9 11 - - - - 11
2
61 10 0 10 - 41 59 0 0 4 0 55
ь : 105 18 0 18 0 69 105 2 0 8 - 95
![Page 9: 04-05-43ep3.nuwm.edu.ua/12130/1/04-05-43 (1).pdf · 2018. 11. 9. · 04-05-43 J H ; H J H = J : F Ь H 2 И K PИ I E 1 GИ Program of the Discipline І g l _ e _ d l m Z eь b c](https://reader036.fdocuments.ec/reader036/viewer/2022062510/611bc9ceb3cd2d1ba369dd90/html5/thumbnails/9.jpg)
9
4. ь
№ /
1. 2 1
2. 2 1
3. ь 2 1
4. ь
2 1
5. 2 1
6. 2 1
7. . 2 1
8. А OLAP- 2 1
9. . . І
ь 2 -
18 8
5.
є (69 .):
1) (0,5 . 1 . ) – 25 .
2) (6 . 1 ) –21 . 3) ,
23 .
№
/
1. І
ь 7 10
2.
7 10
3. ь:
CART, C4.5, CHAID, CN2, NewId, ITrule 7 10
4. :
ь , “ Ч ”,
7 10
5. Agglomerative
Nesting (AGNES), Divisive ANAlysis (DIANA); 8 11
![Page 10: 04-05-43ep3.nuwm.edu.ua/12130/1/04-05-43 (1).pdf · 2018. 11. 9. · 04-05-43 J H ; H J H = J : F Ь H 2 И K PИ I E 1 GИ Program of the Discipline І g l _ e _ d l m Z eь b c](https://reader036.fdocuments.ec/reader036/viewer/2022062510/611bc9ceb3cd2d1ba369dd90/html5/thumbnails/10.jpg)
10
Partitioning Around Medoids (PAM), BIRCH,
CURE, CHAMELEON, ROCK, WaveCluster,
CLARA, Clarans, DBScan
6. Apriori: AprioriTid,
AprioriHybrid, DHP, PARTITION, DIC 8 11
7. К Self-
Organizing Maps (SOM). ь 8 11
8. OLAP- 8 11
9.
ь 9 11
69 . 95 .
“І ” є , . 5. є
. є 4
. є . є ,
.
6.
є , .
є , . ’
, : є ( )
, ’ ; ; .
є , , .
, ,
: , , - , - , , ,
, “ ”, “ ”.
.
![Page 11: 04-05-43ep3.nuwm.edu.ua/12130/1/04-05-43 (1).pdf · 2018. 11. 9. · 04-05-43 J H ; H J H = J : F Ь H 2 И K PИ I E 1 GИ Program of the Discipline І g l _ e _ d l m Z eь b c](https://reader036.fdocuments.ec/reader036/viewer/2022062510/611bc9ceb3cd2d1ba369dd90/html5/thumbnails/11.jpg)
11
є - , є ,
, є
, .
. є , ’ . є
. , є
“ ”.
, .
, ,
’ . -
є , , , . - , , - .
- є
- . , , .
, , є , є
, .
- є , . є -
, є , є
. ( ) ( - ) є ’є 5 – 6
є . – є , , ,
. є є ,
![Page 12: 04-05-43ep3.nuwm.edu.ua/12130/1/04-05-43 (1).pdf · 2018. 11. 9. · 04-05-43 J H ; H J H = J : F Ь H 2 И K PИ I E 1 GИ Program of the Discipline І g l _ e _ d l m Z eь b c](https://reader036.fdocuments.ec/reader036/viewer/2022062510/611bc9ceb3cd2d1ba369dd90/html5/thumbnails/12.jpg)
12
. -
, , ,
, , .
, “ ь” є
, , .
– ,
. “ ь ”.
“ ь” є , є
є . ь – , є
, .
. “ ” є
. є
“ . ”.
“ ” є .
“ ” . є ,
є , , є
. К - – , є
є , , , , .
![Page 13: 04-05-43ep3.nuwm.edu.ua/12130/1/04-05-43 (1).pdf · 2018. 11. 9. · 04-05-43 J H ; H J H = J : F Ь H 2 И K PИ I E 1 GИ Program of the Discipline І g l _ e _ d l m Z eь b c](https://reader036.fdocuments.ec/reader036/viewer/2022062510/611bc9ceb3cd2d1ba369dd90/html5/thumbnails/13.jpg)
13
7.
.
.
: – ; – ,
’ . є ’ .
100- . ,
“І ь ”, є:
, ;
; ( , ,
, ); ґ ’ ; .
100- .
( , ,
) % , , :
0% – ; 40% – є
; 60% – , є
; 80% – ,
є ( , , ); 100% – , .
![Page 14: 04-05-43ep3.nuwm.edu.ua/12130/1/04-05-43 (1).pdf · 2018. 11. 9. · 04-05-43 J H ; H J H = J : F Ь H 2 И K PИ I E 1 GИ Program of the Discipline І g l _ e _ d l m Z eь b c](https://reader036.fdocuments.ec/reader036/viewer/2022062510/611bc9ceb3cd2d1ba369dd90/html5/thumbnails/14.jpg)
14
, ь
7-
( )
1 2
1 2 3 4 5 6 7 8 9
6 6 6 7 7 7 7 7 7 40 100
1, 2,..., 9 .
8. Ш
,
( ),
90 – 100
82-89
74-81
64-73
60-63
35-59
0-34 ’
9.
“І ” є:
1. ( )
, . 2. .
3. . . І - “І ”.
10.
10.1.
1. . ., . . І . :
, 2007. 376 c.
2. ., . Data mining : . . : , 2001. 368 .
![Page 15: 04-05-43ep3.nuwm.edu.ua/12130/1/04-05-43 (1).pdf · 2018. 11. 9. · 04-05-43 J H ; H J H = J : F Ь H 2 И K PИ I E 1 GИ Program of the Discipline І g l _ e _ d l m Z eь b c](https://reader036.fdocuments.ec/reader036/viewer/2022062510/611bc9ceb3cd2d1ba369dd90/html5/thumbnails/15.jpg)
15
3. . І., . . : . . : , 2003.
136 . 4. . . : .
. . : , 2004. 352 . 10.2.
1. . ., . . : . . : , 2004. 424 .
2. , . / . ., . ., . ., . . : , 1997. 112 .
3. . . . . : , 2005. 304 .
4. . ., . . : . . : - , 2001. 382 .
5. . ., . ., . . Soft Computing :
. : - , 2002. 145 .
6. . . . : , 2004. 344 .
7. . . : , , . :
І - , 1999. 320 . 8. ., ., . ,
. . . . . . . : - , 2004. 452 .
9. . ., . . . . . :
- , 2004. 143 . 10. . . . . :
, 2004. 320 . 11. Ч . . Data Mining. .: “ ”, 2016. 471 c. URL:
http://lnfm1.sai.msu.ru/~rastor/Books/Chubukova-Data_Mining.pdf (
: 28.08.2018).
11. І
1. . .І. . URL:
http://www.nbuv.gov.ua/
2. ( . , , 6) / URL : http://www.libr.rv.ua/
3. ( . , . , 44) URL: http://cbs.rv.ua/
4. ( . , . , 75) / URL:
![Page 16: 04-05-43ep3.nuwm.edu.ua/12130/1/04-05-43 (1).pdf · 2018. 11. 9. · 04-05-43 J H ; H J H = J : F Ь H 2 И K PИ I E 1 GИ Program of the Discipline І g l _ e _ d l m Z eь b c](https://reader036.fdocuments.ec/reader036/viewer/2022062510/611bc9ceb3cd2d1ba369dd90/html5/thumbnails/16.jpg)
16
http://nuwm.edu.ua/naukova-biblioteka ( ).