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UNDERSTANDING DATA MINING SOFTWARE II

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UNDERSTANDING DATA MINING SOFTWARE II Ekin Baykal Nikhil Brahmbhatt Jechand Chennupati Joel Edgeman Pushpendra Singh INTRODUCTION Data Mining CRISP-DM Model Teradata ... – PowerPoint PPT presentation

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Title: UNDERSTANDING DATA MINING SOFTWARE II


1
UNDERSTANDING DATA MINING SOFTWARE II
  • Ekin Baykal
  • Nikhil Brahmbhatt
  • Jechand Chennupati
  • Joel Edgeman
  • Pushpendra Singh

2
INTRODUCTION
  • Data Mining
  • CRISP-DM Model
  • Teradata Warehouse Miner
  • Show and Tell

3
INTRODUCTION
  • The best search engine on the internet indexes
    only 16 of the sites. In 1999 the internet
    contained over 15 terabytes of data. (Nature,
    1999b)
  • The quantity of data in GenBank, the
    international repository for genome-sequences
    doubles every 14 months.  (Economist, 1999)
  • The 'Large Hadron Collider' at the CERN will
    generate 20 terabytes of test data each day, for
    the next 15 years.  (Nature, 1999a).  

Source http//www.stt.nl/stt2_intl/projects/datm/
datm.htm
4
DATA MINING
  • The process of identifying and interpreting
    intrinsic patterns in data to solve a business
    problem.

5
HISTORICAL CHALLENGES
  • Lack of standards and business packaging
  • Inability of tools to scale up to the volumes of
    data
  • Noisy, missing, and faulty corporate data
  • Corporate warehousing have been slow to evolve
  • Databases designed for operational processing
    cannot scale up to voluminous analytical
    processing
  • Business doesnt trust results that it cant
    validate/understand
  • Data analysis and mining are typically niche
    oriented processes that exist outside of business
    processes.

6
TODAYS GROWING DEMAND
  • Technological advances in compute power and
    speed
  • Advanced data processing and management
    techniques
  • Greater user sophistication
  • BUT
  • Most tools still work in their own proprietary
    environment
  • Most databases arent optimized for analytic
    processing.
  • Businesses havent integrated data mining and
    knowledge discovery into their workflow.
  • Lack of executive commitment

7
WHERE DOES MINING FIT?
Data Warehouse Data
Name, Addr., Prod.s, Tot., Yrs.
8
WHERE DOES MINING FIT?
The intelligence from the analysis is
incorporated back into the warehouse in the form
of scores, predictions, forecasts, and
descriptions.
9
SUCCESSFUL MINING
  • The right people, an integrated technological
    environment, good tools and sound business
    commitment.
  • To be successful, and profitable, it must a be a
    collaboration driven by the business, developed
    by mining analysts and supported by IT.
  • Good quality data
  • The right tools IT and analysts work together
    to determine which tools work best within the
    technical architecture.

10
THE ANALYTIC ROADMAP
CUSTOMER
MARKETING
SALES
Channel Analysis
Sales Forecast
Loyalty
Buying Prop
Target Marketing
Cross-sell Strategy
Best Campaign
Mkt Basket Analysis
Churn Prop
Satisfact.
Rep Profiling
Best Practices
Profitab.
Lifetime Value
Partner Profiling
Bundling
Campaing Effectiv.
Life Cycle Sequence
EQUIPMENT
PRODUCT
FINANCIAL
Profitab
Retention
Supply/ Demand
Price Point Analysis
Inventory Analysis
Shipper Profiling
Loss
Satisfac.
Bundling
New Product Projections
Timeline Optimiz.
Shipment Analysis
Forecasting
Lifetime Value
Product Optimization
Lifecycle Analysis
Warehouse Optimization
Maintenance Forecast
11
DATA MINING SYSTEMS
  • Four generations of Data Mining Systems
  • First Vector value data
  • Second Databases data warehouses
  • Third Internets and Extranets
  • Fourth Mobile embedded computing devices

Source http//www.lac.uic.edu/grossman/papers/es
j-98.htm
12
CRISP-DM MODEL
  • CRoss Industry Standard Process for Data Mining
  • Non-proprietary, documented, and freely available
    data model
  • Provides Complete blueprint for conducting a
    data mining project
  • Conceived by four leaders of the data mining
    market Daimler-Benz, Integral Solutions, NCR,
    OHRA

13
CRISP-DM MODEL
  • Data Mining process organized into six phases
  • Business understanding
  • Data understanding
  • Data preparation
  • Modeling
  • Evaluation
  • Deployment

14
CRISP-DM REFERENCE MODEL
15
ActiveXTM Private Interface
Client Platform Windows NT 4.0 Windows 2000
Teradata ODBC Driver
Teradata Warehouse Miner Graphical User Interface
Teradata Warehouse Miner Interfaces
TeraMinerTM Stats COM Interface
Analytic Algorithm EXE Server
Matrix Builder EXE Server
Scoring Evaluation EXE Server
Visualization EXE Server
3rd party / NCR CRM applications
Metadata Services
Teradata Platform MP-RAS Windows NT 4.0 Windows
2000
Teradata OLAP and Data Mining Assists
Teradata RDBMS Version 2 Release 3.1 or later
Teradata Data Dictionary
Teradata Source Data
Analytic Metadata
Source Teradata product documentation
16
RESOURCES
  • Data Mining for Enterprise Solutions, Lelia
    Morrill, NCR Corporation, 2001
  • The CRISP-DM Model The New Blueprint for Data
    Mining, Colin Shearer, Journal of Data
    Warehousing, Vol. 5 No. 4, Fall 2000 (Abstract)
  • Data Mining (DATM), http//www.stt.nl/stt2_intl/pr
    ojects/datm/datm.htm
  • Data Rich, Information Poor, http//www.eco.utexas
    .edu/norman/BUS.FOR/course.mat/Alex/
  • There's Gold in that Mountain of Data, Dan R.
    Greening, http//www.newarchitectmag.com/archives/
    2000/01/greening/
  • Supporting the Data Mining Process with Next
    Generation Data Mining Systems, Robert Grossman,
    http//www.lac.uic.edu/grossman/papers/esj-98.htm
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