DM.Lab in University of Seoul Data Mining Laboratory April 24 th, 2008 Summarized by Sungjick Lee An Excel-Based Data Mining Tool iData Analyzer

Data Mining Laboratory 2 DM.Lab in University of Seoul Contents  The iData Analyzer  ESX:A Multipurpose Tool for Data Mining  iDAV Foramt for Data Mining  A Approach for Unsupervised Clustering  A Approach for Supervised Learning

Data Mining Laboratory 3 DM.Lab in University of Seoul The iData Analyzer Scanning for errors illegal numeric values balnk lines missing items allows users to extract a representative subset of the data exemplar-based data mining tool builds a concept hierarchy to generalize data A backpropagation neural network for supervised learning A self-organizing feature map for unsupervised clustering

Data Mining Laboratory 4 DM.Lab in University of Seoul ESX:A Multipurpose Tool for Data Mining(1/2)  Both supervised learning and unsupervised clustering  No statistical assumptions about the nature for data  An automated method for dealing with missing attrib ute values  In domains containg both categorical and numberical data  For supervised classification, Determination of those instances and attributes best able to classify new instances of unknown origin  For unsupervised clustering, a globally optimizing evaluation function that encourages a best instance clustering

Data Mining Laboratory 5 DM.Lab in University of Seoul ESX:A Multipurpose Tool for Data Mining(2/2) define the concept classes summary statistics about the attribute values found within instance-level summary information about the domain Report Generator summary report in spreadsheet format Class resemblance scores

Data Mining Laboratory 6 DM.Lab in University of Seoul iDAV Format for Data Mining C : categorical (nomical) R : real-valued (numerical) I : input attribute U : not used D : not used for classification or clustering, but attribute avlue summary information is displayed O : used as an ouput attribute

Data Mining Laboratory 7 DM.Lab in University of Seoul A Approach for Unsupervised Clustering 1.Enter data into a new Excep Spreadsheet 2.Perform a data mining session 3.Read and interpret summary results 4.Read and interpret results for individual clusters 5.Visualize and interpret rules defining the individual clusters

Data Mining Laboratory 8 DM.Lab in University of Seoul A approach for unsupervised clustering Enter data into a new Excel Spreadsheet  CreditCardPromotion.xls

Data Mining Laboratory 9 DM.Lab in University of Seoul A approach for unsupervised clustering Perform a data mining session(1/2) A value closer to 100 : encourages the formation of new clusters A value closer to 0 : discourages the formation of new clusters The similarity criteria for real-valued attribute 1.0 is usually appropriate 8 classes are too many!! Change Instance similarity value and try again.

Data Mining Laboratory 10 DM.Lab in University of Seoul A approach for unsupervised clustering Perform a data mining session(2/2) Attribute Significance {The largest class mean(class 1 age = 43.33) - The smallest class mean(Class 2 age = 37.00) } / the domain standar deviation

Data Mining Laboratory 11 DM.Lab in University of Seoul A approach for unsupervised clustering Result– RES RUL(The generated production rules) Rules for Class 1Rules for Class 2Rules for Class 3 **Total Percent Coverage = 0.00% Income Range = "20-30,000" :rule accuracy % :rule coverage 80.00% <= Age <= :rule accuracy % :rule coverage 60.00% <= Age <= and Income Range = "20-30,000" :rule accuracy % :rule coverage 60.00% <= Age <= and Magazine Promo = No :rule accuracy % :rule coverage 60.00% ( 중간 생략 ) **Total Percent Coverage = 80.00% Income Range = "30-40,000" :rule accuracy 80.00% :rule coverage 57.14% Magazine Promo = Yes :rule accuracy 75.00% :rule coverage 85.71% Life Ins Promo = Yes :rule accuracy 77.78% :rule coverage % <= Age <= :rule accuracy 77.78% :rule coverage % ( 중간 생략 ) **Total Percent Coverage = %

Data Mining Laboratory 12 DM.Lab in University of Seoul A approach for unsupervised clustering Result– RES SUM(summary statistics) (1/2) Resemblance Score Within-class resemblance scores are higher than the domain resemblance value? If not, why? Bad choice of attributes Bad choice of instances The domain does not contain definable classes Attribute Significance {The largest class mean(class 1 age = 43.33) - The smallest class mean(Class 2 age = 37.00) } / the domain standar deviation (9.51)

Data Mining Laboratory 13 DM.Lab in University of Seoul A approach for unsupervised clustering Result–RES CLS(statistics about the individual class) (1/2) Typicality the average similarity of an instance to all other members of its cluster Predictiveness the state of being predicted the probability an instance reside in the Class between-class measures If ‘1’, the value is sufficient Predictability degree that a correct forecast the percent of instances within a class within-class measur es If ‘1’, the value is necessary

Data Mining Laboratory 14 DM.Lab in University of Seoul A approach for unsupervised clustering Result–RES CLS(statistics about the individual class) (1/2) Highly greater than or equal to 0.80

Data Mining Laboratory 15 DM.Lab in University of Seoul A Approach for Supervised Clustering 1.Enter data into a new Excep Spreadsheet and Choose output attribute 2.Perform a data mining session 3.Read and interpret summary results 4.Read and interpret test set results 5.Read and interpret results for individual clusters 6.Visualize and interpret class rules