Visual Data mining SAS/SPECTRAVIEW Software

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SAS/SPECTRAVIEW Software

Annie Postic / Bengt Bengtsson

SAS Institute

SAS/SPECTRAVIEW Software

Annie Postic / Bengt Bengtsson

SAS Institute

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Visual Data mining

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• SAS/SPECTRAVIEW software

– Advanced Visualization Technology!

• Data Mining

– Turning data into profits!

SAS/SPECTRAVIEW and Data

SAS/SPECTRAVIEW and Data MMiningining

Introduction

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• Business Challenges • Data Mining Solution

• Importance of Data Visualization

• Advanced Visualization Technology

– SAS/SPECTRAVIEW software

• Business Example

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• Turn large quantities of data into meaningful

information

• Turn information into profits

• Gain a competitive advantage !

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• Customer Retention

– 10 times more expensive to acquire new customers than to keep the customers we currently have.

• Profiling/Segmentation

– What are the Traits of Our Most Profitable customers?

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• Cross-Selling

– How can I sell additional products/services to customers based on what they have

already purchased?

• Fraud Detection

– What are the characteristics of a fraudulent transaction?

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• The Data Mining Solution

– SAS Institute defines data mining as :

«The process of selecting, exploring, and modeling large amounts of data to uncover previously unknown patterns for a business advantage»

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• The Data Mining Solution

– These data stockpiles mainly contain customer data, but the data's hidden value--the potential to predict business

trends and customer behavior--has largely gone untapped.

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The Data Mining Process

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ample

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extract portion of data

– Explore -search for patterns/trends

– Modify -reduce # of variables

– Model -analyze data

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The Data Mining Process

SEMMA

– S

ample

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extract portion of data

– Explore -search for patterns/trends – Modify -reduce # of variables

– Model -analyze data

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• Data Visualization Software...

...is one of the most versatile tools for data mining exploration. It enables you to

visually interpret complex patterns in multidimensional data. By viewing data summarized in multiple graphical forms and dimensions, you can uncover trends and spot outliers intuitively and

immediately.

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• Data Visualization Software...

...In the data mining process, visualization tools help you explore data before

modeling--and verify the results of other

data mining techniques. Visualization tools are particularly useful for detecting

patterns found in only small areas of the overall data.

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• SAS/SPECTRAVIEW software

– Advanced Visualization Technology

– Interactive Data Exploration

– 3D Animation and Color Coding

– Integrated component of the SAS System

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SAS/SPECTRAVIEW software

– Explore up to 5 variables at one time using... • Cutting planes • Point Clouds • Volume Rendering 'DWD([SORUDWLRQ 'DWD([SORUDWLRQ

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• SAS/SPECTRAVIEW 6.12 Enhancements

– Categorization - easy to read in data

– Visual Subsetting - easy to capture data

– 3D Probe - easy to pin-point values

– Navigation Tools - easy to manipulate data

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• Health sector

• Business Problem

– High costs for lengthy hospital stays and difficulties to allocate beds

• Business Solution

– Better understand the length of stay to be able to predict the number of occuped bed

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• The Process

– examine characteristics of lengthy hospital stay

• The Tool

– explore data using SAS/SPECTRAVIEW

Example

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• Patient Data from an Hospital

– Characteristics

• Hospital Length of Stay

• Country, City of residence, Age, Origin, Sex etc • 90,000+ observations

• Modified data for confidentiality reasons

Example

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• Examine Data

– Response Variable

• Average Length of Stay

– Independent Variables

• Age • Origin • Country

• City of residence

Example

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• Color Coding -

Response Variable

– Average Length of stay

• > 20 days as Yellow

• 10 - 20 days as Red

• < 10 days as Green

Example

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All Countries, All Ages

by Origin

Average Length of Stay

Countries Countries Age Groups Age Groups 0-110-11 95 + 95 + AVG Stay AVG Stay Origin = Europe Origin = Europe

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All Countries, All Ages

by Origin

Origin = Africa Origin = Africa Countries Countries 0-11 0-11 Age Groups Age Groups 95 + 95 + AVG Stay AVG Stay

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• Narrow in on individual countries • Color Coding -

Response Variable

– Average Length of stay

• > 10 days as Red

• < 10 days as Green

• See if age group is a key attribute in our

modeling process

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All Origins, All Ages

by Coutries

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by Coutries

Origin Origin Age Groups Age Groups 0-11 0-11 95 + 95 + AVG Stay AVG Stay

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by Coutries

Origin Origin Age Groups Age Groups 0-11 0-11 95 + 95 + AVG Stay AVG Stay

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• Identify some exceptionnal long lengths for

young people coming from certain countries

• Long lengths of stay occur at a higher age group

but American people have different behaviour – African people stay longer and are younger than

European people whatever their living country is

– Very few americans living in France going to hospital – No long stays for americans older than 70 years

– European people between 30 and 40 years old coming from France have exceptional

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• Standard statistical analysis

ÖVery similar lengths

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Findings France Italy => 50 years of Age N=712 N=408 Mean=12.3 days Mean=13.4 days < 50 years of Age N=408 N=871 Mean= 9 days Mean= 8 days Switzerland N=4473 Mean=13.4 days N=5034 Mean= 8 days

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• Extract data to Model and continue with the

Data Mining Process

– Handle americans separatly

– Use decision trees to find other determining characteristics (I.e. Medical History, Family background, ...)

– Model the length using influent characteristic – Assess these characteristics for our length

forecasting process

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• Other Visualization methods – Point Clouds – Isosurfaces – Cutting Planes )XUWKHU$QDO\VLV )XUWKHU$QDO\VLV

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Further Analysis

• Point cloud

– PC sales studies

• By date, store and brand

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• Point cloud

– PC sales studies

• By date, store and brand

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• Volume

– Pollution study

• By longitude, latitude, level and time period

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• Isosurface

– Pollution study

• By longitude, latitude, level and time period

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• Visualization of the data helps us to – Better understand data

– Spot patterns and trends not evident in just the numbers

– Discover new relationships

– Save time analyzing your data

• Reveal a subset of attributes to be most productive in the modeling phase of the data mining process

• Intuitive tools for the business professional

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Visual Data mining SAS/SPECTRAVIEW Software

SAS/SPECTRAVIEW Software

Annie Postic / Bengt Bengtsson

SAS Institute

SAS/SPECTRAVIEW Software

Annie Postic / Bengt Bengtsson

SAS Institute

Visual Data mining

SEMMA

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ample

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SEMMA

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Response Variable

Response Variable

– Discover new relationships

Thank you for your attention

Thank you for your attention