Data mining and business intelligence: a guide to productivity
Gespeichert in:
| Hauptverfasser: | , |
|---|---|
| Format: | Buch |
| Sprache: | English |
| Veröffentlicht: |
Hershey, Pa.
Idea Group Publ.
2001
|
| Online-Zugang: | Inhaltsverzeichnis Klappentext |
| Beschreibung: | 250 S. Ill., graph. Darst. |
| ISBN: | 1930708033 9781930708037 |
Internformat
MARC
| LEADER | 00000nam a2200000 c 4500 | ||
|---|---|---|---|
| 001 | BV022268173 | ||
| 003 | DE-604 | ||
| 005 | 20220307 | ||
| 007 | t | ||
| 008 | 070213s2001 ad|| |||| 00||| eng d | ||
| 020 | |a 1930708033 |9 1-930708-03-3 | ||
| 020 | |a 9781930708037 |9 978-1-930708-03-7 | ||
| 035 | |a (OCoLC)635147180 | ||
| 035 | |a (DE-599)BVBBV022268173 | ||
| 040 | |a DE-604 |b ger |e rakwb | ||
| 041 | 0 | |a eng | |
| 049 | |a DE-739 | ||
| 084 | |a QH 500 |0 (DE-625)141607: |2 rvk | ||
| 100 | 1 | |a Kudyba, Stephan |d 1963- |e Verfasser |0 (DE-588)173540384 |4 aut | |
| 245 | 1 | 0 | |a Data mining and business intelligence |b a guide to productivity |c Stephan Kudyba ; Richard Hoptroff |
| 264 | 1 | |a Hershey, Pa. |b Idea Group Publ. |c 2001 | |
| 300 | |a 250 S. |b Ill., graph. Darst. | ||
| 336 | |b txt |2 rdacontent | ||
| 337 | |b n |2 rdamedia | ||
| 338 | |b nc |2 rdacarrier | ||
| 700 | 1 | |a Hoptroff, Richard |e Verfasser |0 (DE-588)1212065220 |4 aut | |
| 856 | 4 | 2 | |m Digitalisierung UB Passau |q application/pdf |u http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=015478706&sequence=000003&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA |3 Inhaltsverzeichnis |
| 856 | 4 | 2 | |m Digitalisierung UB Passau |q application/pdf |u http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=015478706&sequence=000004&line_number=0002&func_code=DB_RECORDS&service_type=MEDIA |3 Klappentext |
| 999 | |a oai:aleph.bib-bvb.de:BVB01-015478706 | ||
Datensatz im Suchindex
| _version_ | 1804136270972059648 |
|---|---|
| adam_text | Data Mining and
Business Intelligence:
A Guide to Productivity
Preface
Part I: Laying the Groundwork
for Data Mining Technology
Chapter
and Business Intelligence
Í)
(IT, Economic Theory and Business Strategy)
II) An Introduction to Business Intelligence
a) The Bl Spectrum: Data Extraction and Report
Writing,
and the Internet
III) Business Intelligence Extended
(An Introduction to Data Mining)...
a) An Introduction to Data Mining Methodologies
b) Visualization Tools for Reporting and
Monitoring: The Humble Chart
IV) The Business Intelligence Cycle
a) Using Mining to Extend
V) Closing Thoughts on Business Intelligence
and Productivity
a) Reducing Uncertainty by Minimizing the Variance
Chapter
I) The Roots of Data Mining
a) A Closer Look at the Mining Process:
(The Traditional Method).....
II) Segmentation and Classification Revisited
III) Neural Network and Regression Mining
(The Robust Approach)
a) A Brief History of Neural Networks
b) Back to Regression and Neural Nets
ÍV)
(A Focus on Neural Nets and Regression)
V) Summing up the Definition of Data Mining
a) A Broad Overview of Data Mining Technologies
Chapter
I) Mining the Right Data (Garbage In, Garbage Out).......
a) How Much Is My Existing Data Worth?
II) Think First, Mine Later (Nine Easy Steps for Success)
III) Pitfalls: Torturing the Data to Get the Answer
You Want to Hear.......
IV) Pitfall: The Pursuit of Statistical Perfection
V) Bridging the Steps to Success to
Applications (Productivity Enhancing Strategies)............
Chapter
I) Forecasting Tools
a) Forecasting: Univariate and Multivariate
II) From Analysis Over Time to Analysis of a
Snapshot in Time
a) Target Measures and Probability Mining......
Part II
in the World of Commerce
Chapter
Promotions and Pricing Policies Using
Econometric Based Modeling
I) Regression/Neural Networks for Marketing
Analysis
II) Measuring the Immediate Impact of Advertising...
III) Measuring the Impact of Price and Promotions
IV) Measuring the Longer Term Impact of Advertising
a) Other Techniques to Measure Longer Term
Impacts of Advertising on Target Measures..
V) A Closer Look at Pricing Strategies
a) Price Optimization Using Cross-Sections:
Data Mining for Pricing...
b) Price Elasticity
c) Finding the Optimum and Break-Even Prices
IV) A Brief Mention of Other Regression
and Neural Network Applications
a) Market Research Analysis
b) Personnel Performance and Retention (HR)
c) Personnel Selection
d)
e) Business Unit Analysis
f) Closing Thoughts
Chapter
into a Click and Mortar by Engage Inc.
I) The Establishment Goes Wired
II) Online Profiling is the Key
III) The
IV) Modify and Convert
V) A Word on Privacy
VI) To Infinity...And Beyond
VII)
Be This Valuable?
Chapter
(A Market Basket Approach) by Macromedia
I) An Introduction to Internet Personalization
a) Basic Personalization
b) Collaborative Filtering Brings New
Dimension of Pattern Recognition
to Personalization
c) Item Affinity: An Extension to Traditional
Market Basket Analysis
d) One Advantage of Market Basket Analysis
Over Item Affinity
e) Item Affinity: Taking the Best of Market
Basket Theory to the Internet
f) How an Implementation of Item Affinity Works
in Comparison to Market Basket Analysis
g) A Recap of Why Item Affinity is Superior to Market Basket
Analysis for Online Marketing.
h) Feature Comparisons of Market Basket Analysis
and an Implementation of Real-Time Item Affinity
II) A More Detailed Description of item Affinity
a) Counting Basics
About Its Tasks
b) Multiple Baskets: Telling Item Affinity How
to Count By Compressing Time
c) Multiple Events: Telling Item Affinity What
to Count by Expanding Events
III) item Affinity in the Real World
IV) Conclusion
Chapter
on an Enterprise Level)
I) The Gap Problems (Communication and Knowledge).....
II) Steps to Achieving a Total Solution with Mining
a) One Model Rarely Captures an Entire Business
Solution: A Human Resource Application
III) Model Optimization and an Introduction to Variance
a) CRM Revisited
b) Other Factors that Promote a Dynamic
Business Environment
c) The Changing Structure of the Economy and
Macro Model Optimization
d) Feedback from Functional Areas..
IV) The Data Mining Solution within the BI Spectrum and
Integration with Other IT Components
VI) Using IT to Survive in the Information Age
Chapter
I) A Quick Word on Innovations in Algorithms
II) The Evolution of E-Business and New Data Mining
a) User Friendly Mining Reports
b) Regression and Neural Network Reports
c) CRM One More Time
III) Continued Streamlining or Simplification
of the Data Access and Extraction Process for Mining
a) Enhancements in Data Storage Techniques
(Warehouses and Marts)
IV) Closing the Gap (The Evolution of the
Workforce and Data Mining Technology)
V) Conclusion
Appendices
About the Authors
Index
Data Mining and Business Intelligence: A Guide to Productivity
provides an overview of data mining technology and how it is
^fpHfSyijfi
sponding data mining methodologies that are used to solve a
variety of business problems thaf enhancelfirm-level effijcijenpy
less technical, more managerial style.
Data Mining and Business Intelligence: A Guide to Productivity
also incorporates the data miningrmethodologies process ¡info
spectrum of complementary technologies that together comprise
corporate information systems and promote business intelli¬
gence.
By connecting technological functionality with prevailing underly¬
ing business applications, this book will show you the full potential
of data mining and business intelligence in* achieving increased
efficiency for firms.
|
| adam_txt |
Data Mining and
Business Intelligence:
A Guide to Productivity
Preface
Part I: Laying the Groundwork
for Data Mining Technology
Chapter
and Business Intelligence
Í)
(IT, Economic Theory and Business Strategy)
II) An Introduction to Business Intelligence
a) The Bl Spectrum: Data Extraction and Report
Writing,
and the Internet
III) Business Intelligence Extended
(An Introduction to Data Mining).
a) An Introduction to Data Mining Methodologies
b) Visualization Tools for Reporting and
Monitoring: The Humble Chart
IV) The Business Intelligence Cycle
a) Using Mining to Extend
V) Closing Thoughts on Business Intelligence
and Productivity
a) Reducing Uncertainty by Minimizing the Variance
Chapter
I) The Roots of Data Mining
a) A Closer Look at the Mining Process:
(The Traditional Method).
II) Segmentation and Classification Revisited
III) Neural Network and Regression Mining
(The Robust Approach)
a) A Brief History of Neural Networks
b) Back to Regression and Neural Nets
ÍV)
(A Focus on Neural Nets and Regression)
V) Summing up the Definition of Data Mining
a) A Broad Overview of Data Mining Technologies
Chapter
I) Mining the Right Data (Garbage In, Garbage Out).
a) How Much Is My Existing Data Worth?
II) Think First, Mine Later (Nine Easy Steps for Success)
III) Pitfalls: Torturing the Data to Get the Answer
You Want to Hear.
IV) Pitfall: The Pursuit of Statistical Perfection
V) Bridging the Steps to Success to
Applications (Productivity Enhancing Strategies).
Chapter
I) Forecasting Tools
a) Forecasting: Univariate and Multivariate
II) From Analysis Over Time to Analysis of a
Snapshot in Time
a) Target Measures and Probability Mining.
Part II
in the World of Commerce
Chapter
Promotions and Pricing Policies Using
Econometric Based Modeling
I) Regression/Neural Networks for Marketing
Analysis
II) Measuring the Immediate Impact of Advertising.
III) Measuring the Impact of Price and Promotions
IV) Measuring the Longer Term Impact of Advertising
a) Other Techniques to Measure Longer Term
Impacts of Advertising on Target Measures.
V) A Closer Look at Pricing Strategies
a) Price Optimization Using Cross-Sections:
Data Mining for Pricing.
b) Price Elasticity
c) Finding the Optimum and Break-Even Prices
IV) A Brief Mention of Other Regression
and Neural Network Applications
a) Market Research Analysis
b) Personnel Performance and Retention (HR)
c) Personnel Selection
d)
e) Business Unit Analysis
f) Closing Thoughts
Chapter
into a Click and Mortar by Engage Inc.
I) The Establishment Goes Wired
II) Online Profiling is the Key
III) The
IV) Modify and Convert
V) A Word on Privacy
VI) To Infinity.And Beyond
VII)
Be This Valuable?
Chapter
(A Market Basket Approach) by Macromedia
I) An Introduction to Internet Personalization
a) Basic Personalization
b) Collaborative Filtering Brings New
Dimension of Pattern Recognition
to Personalization
c) Item Affinity: An Extension to Traditional
Market Basket Analysis
d) One Advantage of Market Basket Analysis
Over Item Affinity
e) Item Affinity: Taking the Best of Market
Basket Theory to the Internet
f) How an Implementation of Item Affinity Works
in Comparison to Market Basket Analysis
g) A Recap of Why Item Affinity is Superior to Market Basket
Analysis for Online Marketing.
h) Feature Comparisons of Market Basket Analysis
and an Implementation of Real-Time Item Affinity
II) A More Detailed Description of item Affinity
a) Counting Basics
About Its Tasks
b) Multiple Baskets: Telling Item Affinity How
to Count By Compressing Time
c) Multiple Events: Telling Item Affinity What
to Count by Expanding Events
III) item Affinity in the Real World
IV) Conclusion
Chapter
on an Enterprise Level)
I) The Gap Problems (Communication and Knowledge).
II) Steps to Achieving a Total Solution with Mining
a) One Model Rarely Captures an Entire Business
Solution: A Human Resource Application
III) Model Optimization and an Introduction to Variance
a) CRM Revisited
b) Other Factors that Promote a Dynamic
Business Environment
c) The Changing Structure of the Economy and
Macro Model Optimization
d) Feedback from Functional Areas.
IV) The Data Mining Solution within the BI Spectrum and
Integration with Other IT Components
VI) Using IT to Survive in the Information Age
Chapter
I) A Quick Word on Innovations in Algorithms
II) The Evolution of E-Business and New Data Mining
a) User Friendly Mining Reports
b) Regression and Neural Network Reports
c) CRM One More Time
III) Continued Streamlining or Simplification
of the Data Access and Extraction Process for Mining
a) Enhancements in Data Storage Techniques
(Warehouses and Marts)
IV) Closing the Gap (The Evolution of the
Workforce and Data Mining Technology)
V) Conclusion
Appendices
About the Authors
Index
Data Mining and Business Intelligence: A Guide to Productivity
provides an overview of data mining technology and how it is
^fpHfSyijfi
sponding data mining methodologies that are used to solve a
variety of business problems thaf enhancelfirm-level effijcijenpy
less technical, more managerial style.
Data Mining and Business Intelligence: A Guide to Productivity
also incorporates the data miningrmethodologies process ¡info
spectrum of complementary technologies that together comprise
corporate information systems and promote "business intelli¬
gence."
By connecting technological functionality with prevailing underly¬
ing business applications, this book will show you the full potential
of data mining and business intelligence in* achieving increased
efficiency for firms. |
| any_adam_object | 1 |
| any_adam_object_boolean | 1 |
| author | Kudyba, Stephan 1963- Hoptroff, Richard |
| author_GND | (DE-588)173540384 (DE-588)1212065220 |
| author_facet | Kudyba, Stephan 1963- Hoptroff, Richard |
| author_role | aut aut |
| author_sort | Kudyba, Stephan 1963- |
| author_variant | s k sk r h rh |
| building | Verbundindex |
| bvnumber | BV022268173 |
| classification_rvk | QH 500 |
| ctrlnum | (OCoLC)635147180 (DE-599)BVBBV022268173 |
| discipline | Wirtschaftswissenschaften |
| discipline_str_mv | Wirtschaftswissenschaften |
| format | Book |
| fullrecord | <?xml version="1.0" encoding="UTF-8"?><collection xmlns="http://www.loc.gov/MARC21/slim"><record><leader>01447nam a2200313 c 4500</leader><controlfield tag="001">BV022268173</controlfield><controlfield tag="003">DE-604</controlfield><controlfield tag="005">20220307 </controlfield><controlfield tag="007">t</controlfield><controlfield tag="008">070213s2001 ad|| |||| 00||| eng d</controlfield><datafield tag="020" ind1=" " ind2=" "><subfield code="a">1930708033</subfield><subfield code="9">1-930708-03-3</subfield></datafield><datafield tag="020" ind1=" " ind2=" "><subfield code="a">9781930708037</subfield><subfield code="9">978-1-930708-03-7</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(OCoLC)635147180</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-599)BVBBV022268173</subfield></datafield><datafield tag="040" ind1=" " ind2=" "><subfield code="a">DE-604</subfield><subfield code="b">ger</subfield><subfield code="e">rakwb</subfield></datafield><datafield tag="041" ind1="0" ind2=" "><subfield code="a">eng</subfield></datafield><datafield tag="049" ind1=" " ind2=" "><subfield code="a">DE-739</subfield></datafield><datafield tag="084" ind1=" " ind2=" "><subfield code="a">QH 500</subfield><subfield code="0">(DE-625)141607:</subfield><subfield code="2">rvk</subfield></datafield><datafield tag="100" ind1="1" ind2=" "><subfield code="a">Kudyba, Stephan</subfield><subfield code="d">1963-</subfield><subfield code="e">Verfasser</subfield><subfield code="0">(DE-588)173540384</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="0"><subfield code="a">Data mining and business intelligence</subfield><subfield code="b">a guide to productivity</subfield><subfield code="c">Stephan Kudyba ; Richard Hoptroff</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="a">Hershey, Pa.</subfield><subfield code="b">Idea Group Publ.</subfield><subfield code="c">2001</subfield></datafield><datafield tag="300" ind1=" " ind2=" "><subfield code="a">250 S.</subfield><subfield code="b">Ill., graph. Darst.</subfield></datafield><datafield tag="336" ind1=" " ind2=" "><subfield code="b">txt</subfield><subfield code="2">rdacontent</subfield></datafield><datafield tag="337" ind1=" " ind2=" "><subfield code="b">n</subfield><subfield code="2">rdamedia</subfield></datafield><datafield tag="338" ind1=" " ind2=" "><subfield code="b">nc</subfield><subfield code="2">rdacarrier</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Hoptroff, Richard</subfield><subfield code="e">Verfasser</subfield><subfield code="0">(DE-588)1212065220</subfield><subfield code="4">aut</subfield></datafield><datafield tag="856" ind1="4" ind2="2"><subfield code="m">Digitalisierung UB Passau</subfield><subfield code="q">application/pdf</subfield><subfield code="u">http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=015478706&sequence=000003&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA</subfield><subfield code="3">Inhaltsverzeichnis</subfield></datafield><datafield tag="856" ind1="4" ind2="2"><subfield code="m">Digitalisierung UB Passau</subfield><subfield code="q">application/pdf</subfield><subfield code="u">http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=015478706&sequence=000004&line_number=0002&func_code=DB_RECORDS&service_type=MEDIA</subfield><subfield code="3">Klappentext</subfield></datafield><datafield tag="999" ind1=" " ind2=" "><subfield code="a">oai:aleph.bib-bvb.de:BVB01-015478706</subfield></datafield></record></collection> |
| id | DE-604.BV022268173 |
| illustrated | Illustrated |
| index_date | 2024-07-02T16:45:10Z |
| indexdate | 2024-07-09T20:53:44Z |
| institution | BVB |
| isbn | 1930708033 9781930708037 |
| language | English |
| oai_aleph_id | oai:aleph.bib-bvb.de:BVB01-015478706 |
| oclc_num | 635147180 |
| open_access_boolean | |
| owner | DE-739 |
| owner_facet | DE-739 |
| physical | 250 S. Ill., graph. Darst. |
| publishDate | 2001 |
| publishDateSearch | 2001 |
| publishDateSort | 2001 |
| publisher | Idea Group Publ. |
| record_format | marc |
| spelling | Kudyba, Stephan 1963- Verfasser (DE-588)173540384 aut Data mining and business intelligence a guide to productivity Stephan Kudyba ; Richard Hoptroff Hershey, Pa. Idea Group Publ. 2001 250 S. Ill., graph. Darst. txt rdacontent n rdamedia nc rdacarrier Hoptroff, Richard Verfasser (DE-588)1212065220 aut Digitalisierung UB Passau application/pdf http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=015478706&sequence=000003&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA Inhaltsverzeichnis Digitalisierung UB Passau application/pdf http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=015478706&sequence=000004&line_number=0002&func_code=DB_RECORDS&service_type=MEDIA Klappentext |
| spellingShingle | Kudyba, Stephan 1963- Hoptroff, Richard Data mining and business intelligence a guide to productivity |
| title | Data mining and business intelligence a guide to productivity |
| title_auth | Data mining and business intelligence a guide to productivity |
| title_exact_search | Data mining and business intelligence a guide to productivity |
| title_exact_search_txtP | Data mining and business intelligence a guide to productivity |
| title_full | Data mining and business intelligence a guide to productivity Stephan Kudyba ; Richard Hoptroff |
| title_fullStr | Data mining and business intelligence a guide to productivity Stephan Kudyba ; Richard Hoptroff |
| title_full_unstemmed | Data mining and business intelligence a guide to productivity Stephan Kudyba ; Richard Hoptroff |
| title_short | Data mining and business intelligence |
| title_sort | data mining and business intelligence a guide to productivity |
| title_sub | a guide to productivity |
| url | http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=015478706&sequence=000003&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=015478706&sequence=000004&line_number=0002&func_code=DB_RECORDS&service_type=MEDIA |
| work_keys_str_mv | AT kudybastephan dataminingandbusinessintelligenceaguidetoproductivity AT hoptroffrichard dataminingandbusinessintelligenceaguidetoproductivity |