Visual Data mining SAS/SPECTRAVIEW Software

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1 Visual Data mining SAS/SPECTRAVIEW Software :HOFRPH Annie Postic / Bengt Bengtsson SAS Institute

2 Introduction SAS/SPECTRAVIEW software Advanced Visualization Technology! Data Mining Turning data into profits!,qwurgxfwlrq

3 Introduction Business Challenges Data Mining Solution Importance of Data Visualization Advanced Visualization Technology SAS/SPECTRAVIEW software Business Example,QWURGXFWLRQ

4 Introduction Turn large quantities of data into meaningful information Turn information into profits Gain a competitive advantage! 7KH&KDOOHQJHV

5 Business Drivers 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? %XVLQHVV'ULYHUV

6 Business Drivers 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? %XVLQHVV'ULYHUV

7 The SAS Solution 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» 7KH6$66ROXWLRQ

8 The SAS Solution 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. 7KH6$66ROXWLRQ

9 The Data Mining Process SEMMA Sample -extract portion of data Explore -search for patterns/trends Modify -reduce # of variables Model -analyze data Assess -determine status and repeat 'DWD0LQLQJ3URFHVV

10 The Data Mining Process SEMMA Sample -extract portion of data Explore -search for patterns/trends Modify -reduce # of variables Model -analyze data Assess -determine status and repeat 'DWD0LQLQJ3URFHVV

11 Data Exploration 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. 'DWD([SORUDWLRQ

12 Data Exploration 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. 'DWD([SORUDWLRQ

13 Data Exploration SAS/SPECTRAVIEW software Advanced Visualization Technology Interactive Data Exploration 3D Animation and Color Coding Integrated component of the SAS System 'DWD([SORUDWLRQ

14 Data Exploration SAS/SPECTRAVIEW software Explore up to 5 variables at one time using... Cutting planes Point Clouds Volume Rendering 'DWD([SORUDWLRQ

15 Data Exploration 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 'DWD([SORUDWLRQ

16 Example 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 ([DPSOH

17 Example The Process examine characteristics of lengthy hospital stay The Tool explore data using SAS/SPECTRAVIEW ([DPSOH

18 Example 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 ([DPSOH

19 Example Examine Data Response Variable Average Length of Stay Independent Variables Age Origin Country City of residence ([DPSOH

20 Example Color Coding - Response Variable Average Length of stay > 20 days as Yellow days as Red < 10 days as Green ([DPSOH

21 All Countries, All Ages by Origin Average Length of Stay Origin = Europe AVG Stay 95 + Age Groups 0-11 Countries

22 All Countries, All Ages by Origin Average Length of Stay Origin = Africa AVG Stay 95 + Age Groups 0-11 Countries

23 Example 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 %XVLQHVV&DVH

24 All Origins, All Ages by Coutries Average Length of Stay

25 All Origins, All Ages by Coutries Average Length of Stay AVG Stay Origin 0-11 Age Groups 95 +

26 All Origins, All Ages by Coutries Average Length of Stay AVG Stay Origin 0-11 Age Groups 95 +

27 Findings 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 long stays )LQGLQJV

28 Findings Standard statistical analysis < 50 years of Age => 50 years of Age France Italy N=408 Mean= 9 days N=871 Mean= 8 days N=712 Mean=12.3 days N=408 Mean=13.4 days Switzerland N=5034 N=4473 Mean= 8 days Mean=13.4 days ÖVery similar lengths )LQGLQJV

29 Conclusion 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 &RQFOXVLRQ

30 Further Analysis Other Visualization methods Point Clouds Isosurfaces Cutting Planes )XUWKHU$QDO\VLV

31 Further Analysis Point cloud PC sales studies By date, store and brand BRAND DATE STORE

32 Further Analysis Point cloud PC sales studies By date, store and brand BRAND DATE STORE

33 Further Analysis Volume Pollution study By longitude, latitude, level and time period

34 Further Analysis Isosurface Pollution study By longitude, latitude, level and time period

35 Conclusion 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 &RQFOXVLRQ

36 Thank you for your attention

Data Mining with SAS. Mathias Lanner mathias.lanner@swe.sas.com. Copyright 2010 SAS Institute Inc. All rights reserved.

Data Mining with SAS. Mathias Lanner mathias.lanner@swe.sas.com. Copyright 2010 SAS Institute Inc. All rights reserved. Data Mining with SAS Mathias Lanner mathias.lanner@swe.sas.com Copyright 2010 SAS Institute Inc. All rights reserved. Agenda Data mining Introduction Data mining applications Data mining techniques SEMMA

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