Indroduction to Data Warehousing Reference book: Data Warehousing, Data Mining and OLAP Alex Berson Stephen J.
Ji aw ei Ha n, Mi che li ne Ka mb er, 'D at a Mi nin g: Co nce pt s and Te chn iqu es ', Mo rg an Kaufmann Publishers, 2002. Alex Be rson,Ste phen J.
Sm ith, “Dat a Warehou sing, Dat a Mining,& OLA P”, Tata Mcg raw- Hill, 2004. Us am a M.F ay ya d, G re go ry Pi at et sk y - S ha pi ro, Pa dh ra i Sm yt h A nd R am as am y Uthurusamy, 'Advances In Knowledge Discovery And Data Mining', The M.I.T Press, 1996. Ralph K imball, 'The Da ta Ware house Lif e Cycle T oolkit', J ohn Wil ey & Sons Inc., 1998. Sean Ke lly, 'Da ta War ehousing In Ac tion', J ohn W iley & Sons I nc., 1997. 1 I n t r o d u c t i o n t o D a t a m i n i n g and knowledge discovery R3, Chapter pp 37-39 1 1 2 R e l a t i o n T o S t a t i s t i c s, Databases R3, Chapter pp 39-40 1 2 3 D a t a M i n i n g F u n c t i o n a l i t i e s R 1, C h a p t e r 1.
4 p p 21-26 1 3 4 S t e p s I n D a t a M i n i n g P r o c e s s R 1, C h a p t e r 1. 2 p p 5-9 1 4 5 A r c h i t e c t u r e O f A T y p i c a l D a t a Mining Systems R1, Chapter 1.2 pp 5-9 1 5 6 C l a s s i f i c a t i o n O f D a t a M i n i n g Systems R1, Chapter 1.6 pp 28-30 1 6 7 O v e r v i e w O f D a t a M i n i n g Techniques R3, Chapter pp 44-48 1 7. PREDICTIVE MODELING 1 6 C l a s s i f i c a t i o n A n d P r e d i c t i o n R 1, C h a p t e r 7. 1 p p 279-282 1 21 1 7 I s s u e s R e g a r d i n g C l a s s i f i c a t i o n And Prediction R1, Chapter 7.2 pp 282-284 1 22 1 8 C l a s s i f i c a t i o n B y D e c i s i o n T r e e Induction R1, Chapter 7.3 pp 284-296 2 24 1 9 B a y e s i a n C l a s s i f i c a t i o n R 1, C h a p t e r 7.
4 p p 296-302 1 25 2 0 O t h e r C l a s s i f i c a t i o n M e t h o d s R 1, C h a p t e r 7. Kisi-kisi soal uas sd kelas 6 2013. 7 p p 314-319 1 26 2 1 P r e d i c t i o n R 1, C h a p t e r 7. 8 p p 319-322 1 27 2 2 C l u s t e r s A n a l y s i s R 1, C h a p t e r 8. 1 p p 335-338 1 28 2 3 T y p e s O f D a t a I n C l u s t e r Analysis R1, Chapter 8.2 pp 338-346 1 29 2 4 C a t e g o r i z a t i o n O f M a j o r Clustering Methods R1, Chapter 8.3 pp 346-348 1 30 2 5 P a r t i t i o n i n g M e t h o d s R 1, C h a p t e r 8.
4 p p 348-354 1 31 2 6 H i e r a r c h i c a l M e t h o d s R 1, C h a p t e r 8. 5 p p 354-363 1 32. DATA WAREHOUSING 2 7 D a t a W a r e h o u s i n g C o m p o n e n t s R 4 C h a p t e r 1 1 3 3 2 8 M u l t i D i m e n s i o n a l D a t a M o d e l R 1 C h a p t e r 2. 2 p p 44-62 2 35 2 9 D a t a W a r e h o u s e A r c h i t e c t u r e R 1 C h a p t e r 2. 3 p p 62 - 71 2 37 3 0 D a t a W a r e h o u s e Implementation R1 Chapter 2.4 pp 71-85 2 38 3 1 M a p p i n g T h e D a t a W a r e h o u s e To Multiprocessor Architecture R2 Part II Chap.
8 pp 15 1-167 1 39 3 2 O L A P. N e e d - R 2 P a r t I I I C h a p. 13 pp 247-266 1 40 3 3 C a t e g o r i z a t i o n O f O L A P T o o l s.
. Title. Data warehousing, data mining, and OLAP / Alex Berson, Stephen J. Also Titled. Data warehousing, data mining & OLAP Author. Berson, Alex. Other Authors.
Smith, Stephen J. Published. New York: McGraw-Hill, c1997. Physical Description. xxvi, 612 p.: ill.; 25 cm. Series. Subjects.
Contents. Ch. Introduction to Data Warehousing. Ch. Client/Server Computing Model and Data Warehousing. Ch.
Parallel Processors and Cluster Systems. Ch. Distributed DBMS Implementations. Ch.
Client/Server RDBMS Solutions. Ch. Data Warehousing Components. Ch. Building a Data Warehouse.
Ch. Mapping the Data Warehouse to a Multiprocessor Architecture. Ch. DBMS Schemas for Decision Support. Ch. Data Extraction, Cleanup, and Transformation Tools. Ch.
Metadata. Ch. Reporting and Query Tools and Applications. Ch. On-Line Analytical Processing (OLAP). Ch.
Patterns and Models. Ch. Statistics. Ch. Artificial Intelligence. Ch.
Introduction to Data Mining. Ch. Decision Trees.
Ch. Neural Networks. Ch. Nearest Neighbor and Clustering. Ch.
Genetic Algorithms. Descargar disco grafia completa de avril. Ch. Rule Induction. Ch.
Selecting and Using the Right Technique. Ch. Data Visualization.
Putting It All Together. App. Big Data - Better Returns: Leveraging Your Hidden Data Assets to Improve ROI. App. Codd's 12 Guidelines for OLAP. App.
10 Mistakes for Data Warehousing Managers to Avoid. Notes. Includes bibliographical references and index. Language.
English ISBN.: Dewey Number. 005.74 Libraries Australia ID.
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Ji aw ei Ha n, Mi che li ne Ka mb er, 'D at a Mi nin g: Co nce pt s and Te chn iqu es ', Mo rg an Kaufmann Publishers, 2002. Alex Be rson,Ste phen J.
Sm ith, “Dat a Warehou sing, Dat a Mining,& OLA P”, Tata Mcg raw- Hill, 2004. Us am a M.F ay ya d, G re go ry Pi at et sk y - S ha pi ro, Pa dh ra i Sm yt h A nd R am as am y Uthurusamy, 'Advances In Knowledge Discovery And Data Mining', The M.I.T Press, 1996. Ralph K imball, 'The Da ta Ware house Lif e Cycle T oolkit', J ohn Wil ey & Sons Inc., 1998. Sean Ke lly, 'Da ta War ehousing In Ac tion', J ohn W iley & Sons I nc., 1997. 1 I n t r o d u c t i o n t o D a t a m i n i n g and knowledge discovery R3, Chapter pp 37-39 1 1 2 R e l a t i o n T o S t a t i s t i c s, Databases R3, Chapter pp 39-40 1 2 3 D a t a M i n i n g F u n c t i o n a l i t i e s R 1, C h a p t e r 1. 4 p p 21-26 1 3 4 S t e p s I n D a t a M i n i n g P r o c e s s R 1, C h a p t e r 1.
2 p p 5-9 1 4 5 A r c h i t e c t u r e O f A T y p i c a l D a t a Mining Systems R1, Chapter 1.2 pp 5-9 1 5 6 C l a s s i f i c a t i o n O f D a t a M i n i n g Systems R1, Chapter 1.6 pp 28-30 1 6 7 O v e r v i e w O f D a t a M i n i n g Techniques R3, Chapter pp 44-48 1 7. PREDICTIVE MODELING 1 6 C l a s s i f i c a t i o n A n d P r e d i c t i o n R 1, C h a p t e r 7. 1 p p 279-282 1 21 1 7 I s s u e s R e g a r d i n g C l a s s i f i c a t i o n And Prediction R1, Chapter 7.2 pp 282-284 1 22 1 8 C l a s s i f i c a t i o n B y D e c i s i o n T r e e Induction R1, Chapter 7.3 pp 284-296 2 24 1 9 B a y e s i a n C l a s s i f i c a t i o n R 1, C h a p t e r 7. 4 p p 296-302 1 25 2 0 O t h e r C l a s s i f i c a t i o n M e t h o d s R 1, C h a p t e r 7. 7 p p 314-319 1 26 2 1 P r e d i c t i o n R 1, C h a p t e r 7.
8 p p 319-322 1 27 2 2 C l u s t e r s A n a l y s i s R 1, C h a p t e r 8. 1 p p 335-338 1 28 2 3 T y p e s O f D a t a I n C l u s t e r Analysis R1, Chapter 8.2 pp 338-346 1 29 2 4 C a t e g o r i z a t i o n O f M a j o r Clustering Methods R1, Chapter 8.3 pp 346-348 1 30 2 5 P a r t i t i o n i n g M e t h o d s R 1, C h a p t e r 8. 4 p p 348-354 1 31 2 6 H i e r a r c h i c a l M e t h o d s R 1, C h a p t e r 8. 5 p p 354-363 1 32. DATA WAREHOUSING 2 7 D a t a W a r e h o u s i n g C o m p o n e n t s R 4 C h a p t e r 1 1 3 3 2 8 M u l t i D i m e n s i o n a l D a t a M o d e l R 1 C h a p t e r 2. 2 p p 44-62 2 35 2 9 D a t a W a r e h o u s e A r c h i t e c t u r e R 1 C h a p t e r 2. 3 p p 62 - 71 2 37 3 0 D a t a W a r e h o u s e Implementation R1 Chapter 2.4 pp 71-85 2 38 3 1 M a p p i n g T h e D a t a W a r e h o u s e To Multiprocessor Architecture R2 Part II Chap.
8 pp 15 1-167 1 39 3 2 O L A P. N e e d - R 2 P a r t I I I C h a p. 13 pp 247-266 1 40 3 3 C a t e g o r i z a t i o n O f O L A P T o o l s.
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