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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

Wismar Business School

Artificial Neural NetworksandData Mining

Uwe Lämmel

www.wi.hs-wismar.de/~laemmel

Uwe.Laemmel@hs-wismar.de

content
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
data mining
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

data mining applications

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
data mining process
Data Mining Process

CRISP-DM model

content6
Data Mining

Classification: approach using NN

Data Mining Cup

Clustering: approach

Content
classification using nn
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

development of an nn application

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
build an artificial neural network
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
experiments using the javanns
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
getting results
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
experiments
Experiments

How can we improve the results?

  • Data pre-processing?
  • Architecture of ANN?
  • Learning Parameters?
  • Evaluation of the results: post-processing?

record your work!

content13
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
data mining cup www data mining cup de
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)
dmc2004 a mailing action
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?
mailing action aim
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
goal function lift
Goal Function: Lift

basis: no mailing action: 9,000 · 72.00

goal = extra income:

liftM = 43.8 · cM + 66.30 · nkM – 72.00· nkM

slide18

----- 32 input data ------

Data

<important

results>

^missing values^

feed forward network what to do
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?
results
gain:

additional income by the mailing actionif target group was chosen according analysis

neural network project 2004

Results

data mining cup 2002

dmc 2007 rebate system
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?
couponing
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!

data understanding
Data Understanding
  • What is the meaning of the attributes?
  • Type and range of values?
20 20 2 network
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)
dmc2008 participation in a lottery
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

slide26
Data
  • 113,476 pattern!
  • 69 attributes
    • new customer (yes/no)
    • age
    • bank
    • car
100 40 20 5 network
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
dmc 2009 online bookshop libri
DMC 2009 – online bookshop „Libri“
  • Sales figures training:
    • more than 1.800 books
    • 2.418 shops
  • Sales figures forecast
    • 8 books
    • 2.394 shops
dmc 2010 revenue maximisation by intelligent couponing
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
dmc 2010
DMC 2010
  • out of 67 teams!
content33
Data Mining

Classification: approach

Data Mining Cup

Clustering: approach

Behaviour of bank customers

Content
clustering transaction data
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
transaction data time series
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
fourier versus original data
Fourier versus Original Data

Data is displaced

frequency spectrum shows similarity

No displacement

Similarity detected on both:

  • transaction curve and
  • frequency spectrum
using a classification model

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
clustering prediction results
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