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Artificial Neural Networks and Data Mining

Wismar Business School. Artificial Neural Networks and Data Mining. Uwe Lämmel. www.wi.hs-wismar.de/~laemmel Uwe.Laemmel@hs-wismar.de. Data Mining Classification: approach Data Mining Cup 2004: Who will cancel? 2007: Who will get a rebate coupon?

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Artificial Neural Networks and Data Mining

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  1. Wismar Business School Artificial Neural NetworksandData Mining Uwe Lämmel www.wi.hs-wismar.de/~laemmel Uwe.Laemmel@hs-wismar.de

  2. Data Mining Classification: approach Data Mining Cup 2004: Who will cancel? 2007: Who will get a rebate coupon? 2008: How long will someone participate in a lottery? 2009: Forecast of book sales figures 2010 ? Clustering: approach Behaviour of bank customers Content

  3. Data Mining Data Mining is a • systematic and automateddiscovery and extraction • of previously unknown knowledge • out of huge amount of data. "KDD – Knowledge Discovery in Data bases" – synonym Notion wrong: Gold Mining  Data Mining

  4. classification • items are placed in subsets (classes) • classes have known properties • customer is bad, average, good • pattern recognition • … • set of training items is used to train the classification algorithm clustering • partitioning a data set into subsets (clusters), so that the data in each subset (ideally) share some common features • similarity or proximity for some defined distance measure • is building classes Data Mining – Applications • classification • clustering • association • prediction • text mining • web mining

  5. Data Mining Process CRISP-DM model

  6. Data Mining Classification: approach using NN Data Mining Cup Clustering: approach Content

  7. Classification using NN training p. prerequisite • set of training pattern (many patterns) approach • code the values • divide set of training pattern into: • training set • test set • build a network • train the network using the training set • check the network quality using the test set coded p. test set training set real data

  8. calculate network output build a network architecture compare to teaching output quality is good enough input of training pattern use Test set data modify weights error is too high evaluate output change parameters compare to teaching output error is too high quality is good enough Development of an NN-application

  9. Build an Artificial Neural Network • Number of Input Neurons? • depends on the number of attributes • depends on the coding • Number of Output Neurons? • depends on the coding of the class attribute • Number of Hidden Neurons? • experiments necessary • generally: not more than input neurons • quarter … half of number of input neurons may work • see capacity of a neural network

  10. Experiments using the JavaNNS • Build a network • Load training-pattern • open the Error Graph • open the Control Panel • Initialize the network • try different learning parameter: 0.1, 0.2, 0.5, 0.8 • Start Learning

  11. Getting Results • value the error • Finally: • make the test-Pattern the actual one • Save Data … • include output files • save as a .res-file • Evaluate the .res-file

  12. Experiments How can we improve the results? • Data pre-processing? • Architecture of ANN? • Learning Parameters? • Evaluation of the results: post-processing? record your work!

  13. Data Mining Classification: approach Data Mining Cup 2004: Who will cancel? 2007: Who will get a rebate coupon? 2008: How long will someone participate in a lottery? 2009: Forecast of book sales figures 2010 ? Clustering: approach Behaviour of bank customers Content

  14. Data Mining Cup www.data–mining–cup.de • annual competition for students • runs April – May /June • real world problem: • problem • set of training data • set of data for classification • to be developed: classification • supported by many companies (data/software) • ~ 200 – 300 participants • workshop (user day)

  15. DMC2004: A Mailing Action • mailing action of a company: • special offer • estimated annual income per customer: • given: • 10,000 sets of customer datacontaining 1,000 cancellers (training) • problem: • test set contains 10,000 customer data • Who will cancel ? • Whom to send an offer?

  16. Mailing Action – Aim? • no mailing action: • 9,000 x 72.00 = 648,000 • everybody gets an offer: • 1,000 x 43.80 + 9,000 x 66.30 = 640,500 • maximum (100% correct classification): • 1,000 x 43.80 + 9,000 x 72.00 = 691,800

  17. Goal Function: Lift basis: no mailing action: 9,000 · 72.00 goal = extra income: liftM = 43.8 · cM + 66.30 · nkM – 72.00· nkM

  18. ----- 32 input data ------ Data <important results> ^missing values^

  19. train the net with training set (10,000) test the net using the test set ( another 10,000) classify all 10,000 customer into canceller or loyal evaluate the additional income Feed Forward Network – What to do?

  20. gain: additional income by the mailing actionif target group was chosen according analysis neural network project 2004 Results data mining cup 2002

  21. DMC 2007: Rebate System Check-out couponing allows an individual coupon generation at the check-out The coupon is printed at the end of the sales slip depending on the current customer. Questions: • How can the retailer identify whether a customer is a potential couponing customer? • On what coupons he will respond?

  22. Couponing • Print: • coupon A • coupon B • No coupon • 50,000 customer cards for training • Classify another 50,000 customer! • Cost function: • coupon not redeemed (false assignment to A or B): –1 • coupon A redeemed (correct assignment to A): +3 • coupon B redeemed (correct assignment to B): +6 Maximize the value!

  23. Data Understanding • What is the meaning of the attributes? • Type and range of values?

  24. 20–20–2 Network Profit = 3AA + 6 BB – (NA+NB+BA+AB) results: • winner 2007 7,890 • my version 6,714 • our students 6,468 (73/230)

  25. DMC2008: Participation in a Lottery Predicting, at the beginning of the lottery, how long participants will participate: • 0 – The first ticket has not been paid for • 1 – Only the ticket for the first class has been paid for • 2 – Only the first two classes were played • 3 – The lottery was played until the end but no ticket purchased for the following lottery • 4 – At least first ticket for the following lottery purchased cost matrix

  26. Data • 113,476 pattern! • 69 attributes • new customer (yes/no) • age • bank • car • …

  27. results: 1,030,240 RWTH Aachen (1) …1,024,535 RWTH Aachen (8) 865,565 Bauhaus Univ. Weimar (100) Univ. Wismar: 878,550 – 835,035 – 1,494,315 (212) 100–40–20–5 Network

  28. DMC 2009 – online bookshop „Libri“ • Sales figures training: • more than 1.800 books • 2.418 shops • Sales figures forecast • 8 books • 2.394 shops

  29. DMC 2009 – online bookshop „Libri“

  30. DMC 2009 – 83-25-9-3 network

  31. DMC 2010: Revenue maximisation by intelligent couponing • Many customers only make an order in an online shop once • decision whether to send a voucher worth € 5.00 • voucher for thosewho would not have decided to re-order by themselves. • 32,427 data sets for training • 32,428 data sets for prediction • 37 attributes per set + target attribute in training set

  32. DMC 2010 • out of 67 teams!

  33. Data Mining Classification: approach Data Mining Cup Clustering: approach Behaviour of bank customers Content

  34. Clustering Transaction Data Co–operation • Hochschule Wismar • HypoVereinsbank • Medienhaus Rostock Issue • What information can be extracted from turnover time series? Strategy • Clustering time series data • Assign customers/accounts to clusters • Examine clusters

  35. Transaction Data & Time Series Corporate clients • 223 branches Cumulated transactions per • Month • Account • Type of transaction ... for a total of 6 years Original financial data not suitable: • Order of values is important • Time displacements are problematic

  36. Fourier versus Original Data Data is displaced frequency spectrum shows similarity No displacement Similarity detected on both: • transaction curve and • frequency spectrum

  37. Customer Turnover ... t0 t0+n tm tm+n Sequence A Sequence B 1. Building the Model 2. Applying themodel Preprocessing Preprocessing Clustering Classification Model Initial Cluster Initial Cluster ? New Cluster 3. Comparing clusterassignments Identical Different Using a classification model

  38. Clustering & Prediction Results • 140.000 records • 1 record = 1 account • 6x5 SOM = max. 30 clusters • average changes of cluster assignments: ca. 19% Variability per Business Sector22,3% Taxi 239/107022,3% Ship Broker Offices 64/47120,9% Churches 228/109120,2% Trucking 1010/5008

  39. Ende

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