using neural networks in database mining tino jimenez cs157b mw 9-10:15 february 19, 2009
TRANSCRIPT
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Using Neural Networks in Database Mining
Tino JimenezCS157B
MW 9-10:15February 19, 2009
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Data Mining Overview
What is Data Mining? The process of extracting values from a
database
Why do we need/use it? Predictive technology Allows for automated decision making
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Data Mining Overview (continued)
What problems does it solve? Stock Market prediction Credit card fraud Loan approval/denial
How does it work? Data analysis of a given set of
information
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Data Mining Tools
Decision Trees A series of rules that allows for
automated decision. Common use: credit card and health insurance approvals
Regression Analysis of the association between a
dependent variable and an independent variable. Common use: prediction
Neural Networks
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The Basis of Neural Networks
Adapted from the research of Artificial Intelligence
Based loosely on the biological functionality of neurons
Mimics the ability to “learn”A neuron is a specialized cell that sends
an electrochemical signal
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The Basis of Neural Networks (cont.)
Each neuron has a specific function and is grouped with other neurons to be able to perform complex tasks
Each neuron has a “weight” which is a determining factor in the importance of the specific function being processed
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How Neural Networks Work
An individual neuron has a step activation function which means that it can have either a -1,0 or 1 value. A value of -1 means that it is an inhibitor and
will lessen the weight of the combined neuronsThe individual neurons are the connected
to each other as inputs and outputs. The inputs carry the values of variables of
interest The outputs form predictions or control signals
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How Neural Networks Work (cont.)
Feedforward Structure The most useful in solving real-world problems Signals flow from inputs through hidden units,
eventually to the output units Input layer is used only to introduce the values
of the input variables The hidden and output layer neurons are each
connected to the all of the units of the preceding layer
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How Neural Networks Work (cont.)
When the network is used, the variable values are placed in the input units and each subsequent layer, calculates the weighted sum of the outputs of the preceding layer until reaching the final layer.
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How Do You Apply a Neural Network
Exact nature of inputs and outputs will be unknown
Large quantities of data are necessary Data can be “noisy”
2 ways to set-up the network Supervised Learning Unsupervised Learning
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Supervised Learning
Data involves historical data sets containing input variables, which correspond to an output
Uses training and testing data to build a model The training data is what the neural network
uses to “learn” how to predict the known output. Also used for validation Famous algorithm is back propagation Uses the data to adjust the weights to minimize the error
in its predictions.
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Unsupervised Learning
Very uncommon to use
Attempts to locate clusters within the input data regardless of variable Supervised Learning only uses input variables
from a training set
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Advantages to Using a Neural Network
High Accuracy Able to approx. complex non-linear mapping
Noise Tolerance Flexible with respect to missing and noisy data
Ease of maintenance Can be implemented in parallel hardware Can be updated with new data, making them
dynamic
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Disadvantages to Using a Neural Network
Poor Transparency Operate as “black boxes” with little/no
knowledge of the algorithms used
Trial-and-Error Design The selection of hidden nodes and training
parameters are heuristic
Data Hungry Requires large amounts of data to be accurate
which also means more computing power
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Applications of Neural Networks
Detection of medical phenomena Recognizes predictive patterns to prescribe
appropriate treatment
Stock market prediction Large numbers of factors are introduced and
used by technical analysts
Credit assignment Identifies most relevant characteristics and
classifies applicants as good or bad credit risks