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NEURAL NETWORKS FOR DATA MINING

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Title: NEURAL NETWORKS FOR DATA MINING


1
Chapter 8
  • NEURAL NETWORKS FOR DATA MINING

2
Learning Objectives
  • ????????????????????????????????????????????
    (artificial neural networks (ANN))
    ???????????????????????
  • ????????????????????????????????????? ANN
  • ??????????????? back propagation neural networks
    ??????????????????
  • ??????????????????????????????????????????????????
    ?
  • ??????????????????????????????????????????????????
    ???

3
Basic Concepts of Neural Networks
  • ?????????????? (NN) ???? ???????????????????
    (artificial neural network (ANN))
  • ?????????????????????????????????????????????????
    ??????????????????????????????????
    ??????????????????????????????????????????????????
    ??????????????????????????????????????????????????
    ???

4
Basic Concepts of Neural Networks
  • ????????????????????? (Neural computing)
  • ?????????????????????????????????????????????????
    ??????????????????????????????????????????????????
    ??????????????????
  • Perceptron
  • ????????????????????????????????????????????????
    (hidden layer)

5
Basic Concepts of Neural Networks
  • ??????????????????????????????? (Biological and
    artificial neural networks)
  • ?????? (Neurons)
  • ????? (????????????? (processing elements)) ???
    biological ???? artificial neural network
  • ????????? (Nucleus)
  • ?????????????????????????
  • ???????? (Dendrite)
  • ??????? biological neuron ???????????????????????
    ?

6
Basic Concepts of Neural Networks
  • ??????? (Axon)
  • ?????????????(i.e., terminal) ??? biological
    neuron
  • ??????? (Synapse)
  • ???????????? (?????????????????????????
    (weights)) ??????????????????????? ?
    ????????????????

7
Basic Concepts of Neural Networks
8
Basic Concepts of Neural Networks
9
Basic Concepts of Neural Networks
  • ????????????? ANN
  • ???????? (Topology)
  • ??????????????????? ? ???????????????????????????
    ?????????? ?
  • ??????????? (Back propagation)
  • ?????????????????????????????????????????????????
    ???????????????? ?????????????????????????????????
    ???????????????????????????????????????????
    (????????????????????)

10
Back Propagation
11
Basic Concepts of Neural Networks
  • ????????????? (Processing elements (PEs))
  • ??????????????
  • ????????????????
  • ???????????????????? (??? 3 ???? ???? three
    layers)
  • ?????? (Input)
  • ???????? (Intermediate layer) ???? ???????
    (hidden layer)
  • ???????? (Output)

12
Basic Concepts of Neural Networks
13
Basic Concepts of Neural Networks
  • ?????????????????? (Parallel processing)
  • ?????????????????????????????????????????????????
    ????????????????????????? ? ??????????????????????
    ? ???? ?????????????????????????? ?

Six specialized vector processors (SPUs)
14
Basic Concepts of Neural Networks
  • ???????????????????????????
  • ?????? (Inputs)
  • ???????? (Outputs)
  • ????????????????????????????? (Connection
    weights)
  • ?????????????? (Summation function) ????
    ??????????????????????(Transformation function)
    ???? ?????????????????? (Transfer function)

15
Basic Concepts of Neural Networks
  • ????????????????????????????? (Connection
    weights)
  • ????????????????????????????? link
    ?????????????????????? ???????????????????????????
    ???????????????????????????????? (neural networks
    learning algorithm)
  • ?????????????? (Summation function) ????
    ??????????????????????(Transformation function)
    ???? ?????????????????? (Transfer function)
  • ???????????????????? ?????????????? (???????)
    ?????????????? ( transform) ?????????????????????
    fire (????????????????) ??????????????????????????
    ? internal activation level ??????????????????????
    ?????

16
Basic Concepts of Neural Networks
17
Basic Concepts of Neural Networks
  • ???????????????? (Sigmoid (logical activation)
    function)
  • S-shaped transfer function ??????????????????? 0
    ??? 1
  • ??????????? (Threshold value)
  • ????????????????????????????????????????
    (trigger) ??????????????????? ????????????????????
    ??????????????????????? ??????????????????????????
    ???????????????????? (???????????????
    ???????????????????????????)
  • ??????? (Hidden layer)
  • ???????? (middle layer) ?????????????????????????
    ???????????????????????????

18
(No Transcript)
19
Sigmoid Function
20
Basic Concepts of Neural Networks
Y Sum of (wixi) (3(0.2) 1(0.4) 2(0.1))
1.2 Transfer function 1/(1exp (-x)) YT
1/(1exp(-1.2)) 0.77
21
  • ???????????????????? ?????????????? Threshold
    0.8 ???? ?????????????? Output ?????
  • ????????????????? Threshold 0.8 ????
    ??????????? Output ?????

22
Basic Concepts of Neural Networks
  • ????????????????????????????
  • ???????????????????????????????? ? ????????????
  • Back propagation
  • Feed forward (or associative memory)
  • Recurrent network

23
Basic Concepts of Neural Networks
24
Basic Concepts of Neural Networks
25
Learning in ANN
  • Learning algorithm
  • ??????????????????????????????? artificial
    neural network
  • Supervised learning
  • ???????????????? (training) ?????????????????????
    ??????????????????????????????????????????????????
    ???????? weights ?????????????????????????????????
    ?????????????????

26
  • Unsupervised learning
  • ?????????????????????????????????????????????????
    ????????????????????????????????
    ??????????????????????????????????????????????????
    ?????

27
Learning in ANN
  • Self-organizing
  • ?????????????????????????????????????????????????
    ? unsupervised learning
  • Adaptive resonance theory (ART)
  • ???????? unsupervised learning ???????????????
    Stephen Grossberg ????????????????????????????????
    ??????????????????????????????????????????????????
    ???????????????? unsupervised mode
  • Kohonen self-organizing feature maps
  • ??????????????????????????????????????? machine
    learning

28
Learning in ANN
29
Learning in ANN
  • ???????????????????? ANN ?????????
    ???????????????????????
  • ????? temporary outputs
  • ??????????? temporary outputs ????????? desired
    targets
  • ????????? weights ?????????????????

30
Learning in ANN
31
Learning in ANN
  • ?????????????? (Pattern recognition)
  • ??????????????????? matching ???????????????
    (external pattern) ???????????????????????????????
    ????????????????? ???????????? inference
    engines, image processing, neural computing, ???
    speech recognition (????????????????????????????
    ????????????? classify data ??????? predetermined
    categories)

32
Learning in ANN
  • ???????????????????????
  • ???????????????? (Learning rate)
  • ???????????????????????????????????????
    ??????????????????????????????????????????????????
    ???????? offset ?????
  • ???????? (Momentum)
  • ??????????????????? (learning parameter)
    ??????????????????? feedforward-backpropagation

33
Learning in ANN
  • Backpropagation
  • ?????????????????????????????????????????????????
    ???????????????????? ?????????????????????????????
    ???????????????????? (computed output)
    ????????????????????? (desired outputs)
    ??????????? historical cases

34
Learning in ANN
  • ??????????????????????????
  • ????????????? learning algorithm
  • ??????????????????? weights ?????? random values
    ?????????????????? ? ?????????????
  • ???? input vector ??? desired output
    ????????????????
  • ???????????????????????????????????????? layer
    ???? ? ???????????????????????? ? ????? (actual
    output)
  • ????????????????????? (error)
  • ????????????????????????? ? (weights)
    ??????????????????? (working backward) ??? output
    layer ????? hidden layers

35
Developing Neural NetworkBased Systems
36
Developing Neural NetworkBased Systems
  • ?????????????????????????????? (Data collection
    and preparation)
  • ??????????????????????????????????? (training and
    testing) ?????????????????????????????????????????
    ??????????
  • ???????????????????????????? (Selection of
    network structure)
  • ??????????? topology ????? ?
  • ???????? (Topology)
  • ?????????????????????????????????????????????????
    ????????? (???????????????????????????????????????
    ????????????????)

37
Developing Neural NetworkBased Systems
  • ???????? topology
  • ?????????????????
  • ?????????????????? (Input nodes)
  • ???????????????????? (Output nodes)
  • ????? hidden layers
  • ???????????????? hidden layer

38
Developing Neural NetworkBased Systems
  • ???????????????????????????? (Learning algorithm
    selection)
  • ??????? set of connection weights ???????????
    training data ???????? ????????? best predictive
    accuracy
  • ?????????????? (Network training)
  • ?????????????? ? ??????????? a random set of
    weights ??? ???? ? ???????????????????????????????
    ???????????????????????????????????????????
    (??????? ??????????????????? ?????????????????????
    ??????)
  • ??????????????????????????????????????????
    (?????????????????????????????????????????????????
    ?????????) ???????????????????????????????????????
    ?????????

39
Developing Neural NetworkBased Systems
  • ???????? (Testing)
  • Black-box testing
  • ?????????????????????????????????????????????????
    ?????????????????????
  • ????????????????????? routine cases ???
    potentially problematic situations
  • ????????????????????????? ?? large deviations
    ??????????????????????????????????????????????????
    ?????? ???????????????????????????????????????????
    ????????????????????????????????

40
Developing Neural NetworkBased Systems
  • ????? ANN ????????
  • ??????????????????????????????????????????????????
    ???????????????????????? ? ???????????????????????
    ??????????????
  • ???????? Ongoing monitoring ??? feedback
    ??????????????????????????????????????????????????
    ??????????????
  • ??????????????????????????????????????????????????
    ??????????????????????????????????????????????????
    ??????????????????????????????????????????????????
    ?

41
Developing Neural NetworkBased Systems
42
A Sample Neural Network Project
43
Other Neural Network Paradigms
  • Hopfield networks
  • A single large layer of neurons
    ????????????????????????????????? (total
    interconnectivity) ??????? ???????????????????????
    ???????????? ? ??????
  • ????????????????????????????????????????????????
  • ????????????????? Hopfield networks ?????
    ?????????????????? constrained optimization
    problem ???? classic traveling salesman problem
    (TSP)

44
Other Neural Network Paradigms
  • Self-organizing networks
  • Kohonens self-organizing network ??????????????
    unsupervised mode
  • ????????????? Kohonen ??????????????? feature
    maps ??????? neighborhoods of neurons
    ??????????????
  • Neighborhood ???????????????????????????????
    topology ?????????????????????????????????????????
    ????????????????????
  • Self-organizing maps ???? self organizing feature
    maps ?????????????????????????? some early
    insight into the data

45
(No Transcript)
46
Applications of ANN
  • ANN ??????????????????? ? ?????????????????
    categorical ??? numeric ??????????????????????????
    ??????????????????????????????????? ????
    ???????????????????????????????????????

47
??????????? 8
  • ???????????????.
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