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7/30/2019 data mining using neural networks
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Major StepsNetwork Construction and Training
Network PruningRule extraction
Traditional approaches VS. NEURAL NETWORKSConclusionReferences
Neural Network Model For Data Mining
Lubna Shaikh
Guided By- Prof Jitali Patel
January 2013
Computer Science Department Seminar
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Major StepsNetwork Construction and Training
Network PruningRule extraction
Traditional approaches VS. NEURAL NETWORKSConclusionReferences
1 Major Steps2 Network Construction and Training
Is Neural Network Approach Appropriate?Select Appropriate ParadigmSelect Input Data and FactsData PreparationTraining Strategies
3 Network Pruning
4 Rule extraction
Rule Extraction Algorithm
5 Traditional approaches VS. NEURAL NETWORKS
6 Conclusion
7 References
Computer Science Department Seminar
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Major StepsNetwork Construction and Training
Network PruningRule extraction
Traditional approaches VS. NEURAL NETWORKSConclusionReferences
The following are the major steps required to develop aNeural Network Model for Data Mining:
Network Construction and Training
Network Pruning
Rule Extraction
Knowledge Representation
Computer Science Department Seminar
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Major StepsNetwork Construction and Training
Network PruningRule extraction
Traditional approaches VS. NEURAL NETWORKSConclusionReferences
Is Neural Network Approach Appropriate?Select Appropriate ParadigmSelect Input Data and FactsData PreparationTraining Strategies
Outline1 Major Steps
2 Network Construction and TrainingIs Neural Network Approach Appropriate?
Select Appropriate ParadigmSelect Input Data and FactsData PreparationTraining Strategies
3 Network Pruning
4 Rule extractionRule Extraction Algorithm
5 Traditional approaches VS. NEURAL NETWORKS
6 Conclusion
7 ReferencesComputer Science Department Seminar
M j S
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Major StepsNetwork Construction and Training
Network PruningRule extraction
Traditional approaches VS. NEURAL NETWORKSConclusionReferences
Is Neural Network Approach Appropriate?Select Appropriate ParadigmSelect Input Data and FactsData PreparationTraining Strategies
Steps Involved
Is Neural Network approach appropriate?
Select appropriate Paradigm
Select input data and facts
Prepare data
Train and test networkUse the Network for Data Mining
Computer Science Department Seminar
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Major StepsNetwork Construction and Training
Network PruningRule extraction
Traditional approaches VS. NEURAL NETWORKSConclusionReferences
Is Neural Network Approach Appropriate?Select Appropriate ParadigmSelect Input Data and FactsData PreparationTraining Strategies
Is Neural Network Approach Appropriate?
Inadequate knowledge base
Volatile knowledge basesData-intensive system
Standard technology is inadequate
Qualitative or complex quantitative reasoning is required
Data is intrinsically noisy and error-proneProject development time is short and training time for theneural network is reasonable.
Computer Science Department Seminar
Major Steps
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Major StepsNetwork Construction and Training
Network PruningRule extraction
Traditional approaches VS. NEURAL NETWORKSConclusionReferences
Is Neural Network Approach Appropriate?Select Appropriate ParadigmSelect Input Data and FactsData PreparationTraining Strategies
Select Appropriate Paradigm
Decide Network architecture according to general problemarea
Classification
FilteringPattern recognitionOptimizationData compressionPrediction
Select network sizeNo. of inputsNo. of outputsNo. of hidden layersNo. of neurons per layer
Computer Science Department Seminar
Major Steps
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Major StepsNetwork Construction and Training
Network PruningRule extraction
Traditional approaches VS. NEURAL NETWORKSConclusionReferences
Is Neural Network Approach Appropriate?Select Appropriate ParadigmSelect Input Data and FactsData PreparationTraining Strategies
Select Appropriate Paradigm
Decide on learning method
Decide on transfer function
Decide on nature of input/output
Decide on type of training used.
Computer Science Department Seminar
Major Steps
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Major StepsNetwork Construction and Training
Network PruningRule extraction
Traditional approaches VS. NEURAL NETWORKSConclusionReferences
Is Neural Network Approach Appropriate?Select Appropriate ParadigmSelect Input Data and FactsData PreparationTraining Strategies
Data Set Considerations
Size
Noise
Knowledge domain representation
Training set and test set
Insufficient dataCoding the input data
Computer Science Department Seminar
Major Steps
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Major StepsNetwork Construction and Training
Network PruningRule extraction
Traditional approaches VS. NEURAL NETWORKSConclusionReferences
Is Neural Network Approach Appropriate?Select Appropriate ParadigmSelect Input Data and FactsData PreparationTraining Strategies
Data Set Size
Optimal size of the training set depends on the type ofnetwork used.
Size is relatively large
Rule of thumb for backpropagation networks:
Training Set Size = Number of hidden layers/Testing
Tolerance + Number of input neuron
Computer Science Department Seminar
Major Steps
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Major StepsNetwork Construction and Training
Network PruningRule extraction
Traditional approaches VS. NEURAL NETWORKSConclusionReferences
Is Neural Network Approach Appropriate?Select Appropriate ParadigmSelect Input Data and FactsData PreparationTraining Strategies
Noise and Knowledge Domain Representation
For back propagation networks, the training is more successfulwhen the data contain noise.
Training set should contain a good representation of theentire universe of the domain
May result in an increase in number of training facts, causingthe network size to change.
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Major Steps
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j pNetwork Construction and Training
Network PruningRule extraction
Traditional approaches VS. NEURAL NETWORKSConclusionReferences
Is Neural Network Approach Appropriate?Select Appropriate ParadigmSelect Input Data and FactsData PreparationTraining Strategies
Selection of Variables
Reduce the size of input data without degrading theperformance of the network
Principle Component AnalysisManual Method
Computer Science Department Seminar
Major Steps
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Network Construction and TrainingNetwork Pruning
Rule extractionTraditional approaches VS. NEURAL NETWORKS
ConclusionReferences
Is Neural Network Approach Appropriate?Select Appropriate ParadigmSelect Input Data and FactsData PreparationTraining Strategies
Insufficient Data
Scarce data makes the allocation of the data into training anda testing set critical
Rotation Scheme:
Data set has N facts
Set aside one of the facts, training the system with N-1 facts.
Then set aside another fact and retrain the network with the
other N-1 facts.
Repeat the process N times.
Made-up Data:Include made up-data, idea of BOOTSTRAPPING is also used
The decision should be made as whether the distribution of
data should be maintained.
Expert-made Data
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Major Steps
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Network Construction and TrainingNetwork Pruning
Rule extractionTraditional approaches VS. NEURAL NETWORKS
ConclusionReferences
Is Neural Network Approach Appropriate?Select Appropriate ParadigmSelect Input Data and FactsData PreparationTraining Strategies
Coding the Input Data
The training data set should be properly normalized. andmatch the design of the network.
Functions used:
Zero-mean-unit Variant (Zscore)Min-Max
Cut offSigmoidale
Computer Science Department Seminar
Major StepsN k C i d T i i I N l N k A h A i ?
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Network Construction and TrainingNetwork Pruning
Rule extractionTraditional approaches VS. NEURAL NETWORKS
ConclusionReferences
Is Neural Network Approach Appropriate?Select Appropriate ParadigmSelect Input Data and FactsData PreparationTraining Strategies
Data Preparation
In Distributed data set, the qualities that define a uniquepattern are spread out over more than one neuron
For example, a purple object can be represented described asbeing half red and half blue
The two neurons assigned to red and blue can together define
purple, eliminating the need to assign a third purple neuron
Computer Science Department Seminar
Major StepsN t k C t ti d T i i I N l N t k A h A i t ?
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Network Construction and TrainingNetwork Pruning
Rule extractionTraditional approaches VS. NEURAL NETWORKS
ConclusionReferences
Is Neural Network Approach Appropriate?Select Appropriate ParadigmSelect Input Data and FactsData PreparationTraining Strategies
Training StrategiesThe main intention of training is not to memorize theexamples of the training set, but to build a general model ofthe input/output relationships based on the training examples.
GeneralisationA general model means that the set of input/outputrelationships, derived from the training set, apply equally wellto new sets of data from the same problem not included in thetraining set.The main goal of a neural network is thus the generalization tonew data of the relationships learned on the training set.
OverfittingA too large amount of training can memorize all the examplesof the training set with the associated noise, errors, andinconsisten- cies, and therefore perform a poor generalization
on new data.Computer Science Department Seminar
Major StepsNetwork Construction and Training Is Neural Network Approach Appropriate?
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Network Construction and TrainingNetwork Pruning
Rule extractionTraditional approaches VS. NEURAL NETWORKS
ConclusionReferences
Is Neural Network Approach Appropriate?Select Appropriate ParadigmSelect Input Data and FactsData PreparationTraining Strategies
Network Dimension
The overfitting problem depends on the model size, the
number of free parameters, the number of constraints, thenumber of independent training examples.
A rule of thumb for obtaining good generalization is to usethe smallest network that fits the training data.
A small network, besides the better expected generalization, isalso faster to train.
Computer Science Department Seminar
Major StepsNetwork Construction and Training
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Network Construction and TrainingNetwork Pruning
Rule extractionTraditional approaches VS. NEURAL NETWORKS
ConclusionReferences
Outline1 Major Steps
2 Network Construction and TrainingIs Neural Network Approach Appropriate?Select Appropriate ParadigmSelect Input Data and FactsData PreparationTraining Strategies
3 Network Pruning
4 Rule extractionRule Extraction Algorithm
5 Traditional approaches VS. NEURAL NETWORKS
6 Conclusion
7
ReferencesComputer Science Department Seminar
Major StepsNetwork Construction and Training
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Network Construction and TrainingNetwork Pruning
Rule extractionTraditional approaches VS. NEURAL NETWORKS
ConclusionReferences
Network pruning
Pruning Algorithms
The general pruning approach consists of training a relativelylarge network and gradually removing either weights orcomplete units that seem not to be necessary.
The large initial size allows the network to learn quickly andwith a lower sensitivity to initial conditions and local minima.
The reduced final size helps to improve generalization. Thereare basically two ways of reducing the size of the originalnetwork.
Computer Science Department Seminar
Major StepsNetwork Construction and Training
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Network Construction and TrainingNetwork Pruning
Rule extractionTraditional approaches VS. NEURAL NETWORKS
ConclusionReferences
Network pruning
Sensitivity methods: After learning the sensitivity of the errorfunction to the removal of every element (unit or weight) is
esti- mated: the element with the least effect can be removed.Penalty-term methods: Weight decay terms are added to theerror function, to reward the network for choosing efficientsolutions. That is networks with small weight values areprivileged. At the end of the learning process, the weightswith smallest values can be removed, but, even in case theyare not, a network with several weights close to 0 already actsas a smaller system.
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Major StepsNetwork Construction and Training
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gNetwork Pruning
Rule extractionTraditional approaches VS. NEURAL NETWORKS
ConclusionReferences
Rule Extraction Algorithm
Outline1 Major Steps
2 Network Construction and TrainingIs Neural Network Approach Appropriate?Select Appropriate ParadigmSelect Input Data and FactsData PreparationTraining Strategies
3 Network Pruning
4 Rule extractionRule Extraction Algorithm
5 Traditional approaches VS. NEURAL NETWORKS
6 Conclusion
7 References
Computer Science Department Seminar
Major StepsNetwork Construction and Training
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gNetwork Pruning
Rule extractionTraditional approaches VS. NEURAL NETWORKS
ConclusionReferences
Rule Extraction Algorithm
Rule extraction
Extracts classification rules from pruned network
Rules generated are in the form of if (a1, v1,) and (x1, v1,)and ... and (xn, vn,) then Cj
Where a is are the attributes of an input tuple
vis are constants
s are relational operators (=,, !=)Cj is one of the class labels
Computer Science Department Seminar
Major StepsNetwork Construction and Training
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Network PruningRule extraction
Traditional approaches VS. NEURAL NETWORKSConclusionReferences
Rule Extraction Algorithm
Difficulties in defining relationships:
Links may be still too many to express the relationshipbetween an input tuple and its class label in the form of if ...then rules.
If a network has n input links with binary values, there couldbe as many as 2n, distinct input patterns.
The rules could be quite lengthy or complex even for a small n.
The activation values of a hidden unit could be anywhere in
the range [-1, 1] depending on the input tuple.
Difficult to derive an explicit relationship between thecontinuous activation values of the hidden units and theoutput values of a unit in the output layer.
Computer Science Department Seminar
Major StepsNetwork Construction and Training
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Network PruningRule extraction
Traditional approaches VS. NEURAL NETWORKSConclusionReferences
Rule Extraction Algorithm
Rule Extraction Algorithm, (RX)
1 Apply a clustering algorithm to find clusters of hidden nodeactivation values.
2 Enumerate the discretized activation values and compute thenetwork outputs.
3 Generate rules that describe the network outputs in terms ofthe discretized hidden unit activation values.
4 For each hidden unit, enumerate the input values that lead to
them and generate a set of rules to describe the hidden unitsdiscretized values in terms of the inputs.
5 Merge the two sets of rules obtained in the previous two stepsto obtain rules that relate the inputs and outputs.
Computer Science Department Seminar
Major StepsNetwork Construction and Training
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Network PruningRule extraction
Traditional approaches VS. NEURAL NETWORKSConclusionReferences
Rule Extraction Algorithm
Clustering1st step of RX clusters the activation values of hidden unitsinto a manageable number of discrete values
Without sacrificing the classification accuracy of the network
Neural Network based clustering method
Represents each cluster as an exemplar which acts as aprototype
New objects can be distributed to the cluster whose exemplar
is the most similar based on some distance measure.Neural network approach has strong theoretical links withactual brain processing.
Competitive LearningSelf-organizing feature maps
Computer Science Department Seminar
Major StepsNetwork Construction and Training
N t k P i
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Network PruningRule extraction
Traditional approaches VS. NEURAL NETWORKSConclusionReferences
Rule Extraction Algorithm
Competitive Learning
Hierarchical architecture of several artificial Neurons
Winner-takes-all fashionWinning unit within each cluster becomes active(filled circles)while others are inactive.
Connections between layers are excitatory- inputs are receivedfrom lower levels.
The units within a cluster compete to responds to the patternthat is output from the layer below.
Computer Science Department Seminar
Major StepsNetwork Construction and Training
Network Pruning
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Network PruningRule extraction
Traditional approaches VS. NEURAL NETWORKSConclusionReferences
Rule Extraction Algorithm
Competitive Learning
Connections within layers are inhibitory so that only 1 unit in
a given cluster may be active.The winning unit adjusts the weights on its connectionsbetween other units in the cluster so that it will respond morestrongly in future.
The number of clusters and the number of units per clusterare input parameters.
Computer Science Department Seminar
Major StepsNetwork Construction and Training
Network Pruning
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Network PruningRule extraction
Traditional approaches VS. NEURAL NETWORKSConclusionReferences
Rule Extraction Algorithm
Competitive Learning
Figure: Competitive LearningComputer Science Department Seminar
Major StepsNetwork Construction and Training
Network Pruning
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Network PruningRule extraction
Traditional approaches VS. NEURAL NETWORKSConclusionReferences
Rule Extraction Algorithm
Self Organising feature maps(SOMS)
The learning algorithm in SOM still follows the competitivemodel, but the updating rule produces an output layer, wherethe topology of the patterns in the input space is preserved.
That means that if patterns xr and xs are close in the inputspace - close on the basis of the similarity measure adopted inthe winner-take-all rule- the corresponding firing neural unitsare topologically close in the network layer.
A network that performs such a mapping is called a featuremap. Feature maps not only group input patterns intoclusters, but also visually describe the relationships amongthese clusters in the input space.
Computer Science Department Seminar
Major StepsNetwork Construction and Training
Network Pruning
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Network PruningRule extraction
Traditional approaches VS. NEURAL NETWORKSConclusionReferences
Rule Extraction Algorithm
Self Organising feature maps(SOMS)A Kohonen map consists usually of a two-dimensional array ofneurons fully connected with the input vector, without lateralconnections, arranged on a squared or hexagonal lattice.
The topology preserving property is obtained by a learningrule that involves the winner unit and its neighbors in theweight updating process.
As a consequence, close neurons in the output layer learn to
fire for input vectors with similar characteristics.During training the network assigns to firing neurons aposition on the map, based on the dominant feature of theactivating input vector. For this reason Kohonen maps arealso called Self-Organizing Maps(SOM).
Computer Science Department Seminar
Major StepsNetwork Construction and Training
Network Pruning
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gRule extraction
Traditional approaches VS. NEURAL NETWORKSConclusionReferences
Rule Extraction Algorithm
Competitive Learning
Figure: Self Organizing Maps (SOMS)Computer Science Department Seminar
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Network Pruning
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gRule extraction
Traditional approaches VS. NEURAL NETWORKSConclusionReferences
Rule Extraction Algorithm
Rule Extraction Algorithm
2nd step is to relate these discretized activation values withthe output layer activation values, i e, the class labels.
3rd step is to relate them with the attribute values at thenodes connected to the hidden node.
Input:
Set of discrete patterns with the class labels and produces therules describing the relationship between the patterns andtheir class labels
Computer Science Department Seminar
Major StepsNetwork Construction and Training
Network Pruning
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Rule extractionTraditional approaches VS. NEURAL NETWORKS
ConclusionReferences
Rule Extraction Algorithm
Knowledge Extraction
One of the reasons of Neural Networks success is its ability todevelop an internal representation of the knowledge necessaryto solve a given problem.
However, such internal knowledge representation is verydifficult to understand and to translate into symbolicknowledge, due to its distributed nature.
At the end of the learning process, the networks knowledge is
spread all over its weights and units.In addition, even if a translation into symbolic rules is possibleit might not have physical meaning, because the networkscom putation does not take into account the physical rangesof the input variables.
Computer Science Department Seminar
Major StepsNetwork Construction and Training
Network Pruningi i A i
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Rule extractionTraditional approaches VS. NEURAL NETWORKS
ConclusionReferences
Rule Extraction Algorithm
Knowledge Extraction
Generally the internal knowledge representation of neuralnetworks presents a very low degree of human
comprehensibility and, for this reason, it has often beendescribed as opaque to the outside world.
This lack of comprehension of how decisions are made inside aneural network definitely represents a strong limitation for the
application of ANNs to intelligent data analysis.Several real world applications need an explanation of how agiven decision is reached.
Computer Science Department Seminar
Major StepsNetwork Construction and Training
Network PruningR l t ti R l E t ti Al ith
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Rule extractionTraditional approaches VS. NEURAL NETWORKS
ConclusionReferences
Rule Extraction Algorithm
Advantages Of Neural Networks?
High Accuracy: Neural networks are able to approximatecomplex non-linear mappings.
Noise Tolerance: Neural networks are very flexible withrespect to incomplete, missing and noisy data.
Independence from prior assumptions:
Neural networks can be updated with fresh data, making themuseful for dynamic environments.
Hidden nodes, in supervised neural networks can be regardedas latent variables.
Neural networks can be implemented in parallel hardware
Computer Science Department Seminar
Major StepsNetwork Construction and Training
Network PruningR le extraction R le Extraction Algorithm
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Rule extractionTraditional approaches VS. NEURAL NETWORKS
ConclusionReferences
Rule Extraction Algorithm
Disadvantages Of Neural Networks?
One of the main drawbacks of ANN paradigms consists of thelack of criteria for the a priori definition of the optimal
network size for a given task.The space generated by all possible ANN structures withdifferent size for a selected ANN paradigm can then becomethe object of other data analysis techniques.
Genetic algorithms, for example, have been recently applied tothis problem, to build a population of good ANN architectureswith respect to a given task.
Computer Science Department Seminar
Major StepsNetwork Construction and TrainingNetwork Pruning
Rule extraction Rule Extraction Algorithm
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Rule extractionTraditional approaches VS. NEURAL NETWORKS
ConclusionReferences
Rule Extraction Algorithm
Disadvantages Of Neural Networks?
ANNs decision processes remain still quite opaque and atrans- lation into meaningful symbolic knowledge hard to
perform.On the contrary, fuzzy systems are usually appreciated for thetransparency of their decisional algorithms.
The combination of the ANN approach and of fuzzy logic has
produced hybrid architectures, called neuro-fuzzy networks.Learning rules are no more constrained into the traditionalcrisp logic, but exploit the linguistic power of fuzzy logic.
Computer Science Department Seminar
Major StepsNetwork Construction and TrainingNetwork Pruning
Rule extraction
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Rule extractionTraditional approaches VS. NEURAL NETWORKS
ConclusionReferences
Outline
1 Major Steps
2 Network Construction and TrainingIs Neural Network Approach Appropriate?Select Appropriate Paradigm
Select Input Data and FactsData PreparationTraining Strategies
3 Network Pruning
4 Rule extractionRule Extraction Algorithm
5 Traditional approaches VS. NEURAL NETWORKS
6 Conclusion
7 References
Computer Science Department Seminar
Major StepsNetwork Construction and TrainingNetwork Pruning
Rule extraction
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Rule extractionTraditional approaches VS. NEURAL NETWORKS
ConclusionReferences
Traditional approaches of Data Mining VS. NeuralNetworks
Foundation: Logic vs. Brain
Traditional Approach: Simulate and formalize human reasoningand logic process. TA treats the brain as a black box.TA focuses on how the elements are related to each other andhow to give the machine the same capabilities.Neural Networks: Simulate the intelligence functions of the
brain. NN focus on modeling the brain structure.NN attempts to create a system that functions like the brainbecause it has a structure similar to the structure of the brain
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Major StepsNetwork Construction and TrainingNetwork Pruning
Rule extraction
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Rule extractionTraditional approaches VS. NEURAL NETWORKS
ConclusionReferences
Processing Techniques: Sequential vs. Parallel
Traditional Approach: The processing method of TA is
inherently sequential.
Neural Networks: The processing method of NN is inherentlyparallel.
Each neuron in a neural network system functions in parallel
with others
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Rule extraction
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Traditional approaches VS. NEURAL NETWORKSConclusionReferences
Learning: Static and External vs. Dynamic and Internal
Traditional Approach: Learning takes place outside of thesystem.
The knowledge is obtained outside the system and then codedinto the system.
Neural Networks: Learning is an integral part of the systemand its design.
Knowledge is stored as the strength of the connections amongthe neurons and it is the job of NN to learn these weightsfrom a data set presented to it.
Computer Science Department Seminar
Major StepsNetwork Construction and TrainingNetwork Pruning
Rule extraction
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Traditional approaches VS. NEURAL NETWORKSConclusionReferences
Reasoning Method: Deductive vs. Inductive
Traditional Approach: Is deductive in nature.
The use of the system involves a deductive reasoning process,
applying the generalized knowledge to a given case.Neural Networks: Is inductive in nature.
It constructs an internal knowledge base from the datapresented to it.
It generalizes from the data, such that when it is presented anew set of data, it can make a decision based on thegeneralized internal knowledge.
Computer Science Department Seminar
Major StepsNetwork Construction and TrainingNetwork Pruning
Rule extraction
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Traditional approaches VS. NEURAL NETWORKSConclusionReferences
Knowledge Representation: Explicit vs. Implicit
Traditional Approach: It represents knowledge in an explicit
form.Rules and relationships can be inspected and altered.
Neural Networks: The knowledge is stored in the form ofinterconnections strengths among neurons
Nowhere in the system, can one pick up a piece of computercode or a numerical value as a discernible piece of knowledge.
Computer Science Department Seminar
Major StepsNetwork Construction and TrainingNetwork Pruning
Rule extractionT di i l h VS NEURAL NETWORKS
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Traditional approaches VS. NEURAL NETWORKSConclusionReferences
Outline
1 Major Steps
2 Network Construction and TrainingIs Neural Network Approach Appropriate?Select Appropriate Paradigm
Select Input Data and FactsData PreparationTraining Strategies
3 Network Pruning
4 Rule extractionRule Extraction Algorithm
5 Traditional approaches VS. NEURAL NETWORKS
6 Conclusion
7 References
Computer Science Department Seminar
Major StepsNetwork Construction and TrainingNetwork Pruning
Rule extractionT diti l h VS NEURAL NETWORKS
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Traditional approaches VS. NEURAL NETWORKSConclusionReferences
Conclusion
A study of the neural network based data mining process show thatneural network is very suitable for solving the problems of datamining because its characteristics of good robustness,
self-organizing adaptive, parallel processing, distributed storage andhigh degree of fault tolerance. The combination of data miningmethod and neural network model can greatly improve theefficiency of data mining methods, and it has been widely used.One of the issues is to reduce the training time of neural networks.
The speed of network training by developing fast algorithms can beimproved. Tthe time required to extract rules by neural networkapproach is still longer than the time needed by the decision treebased approach.
Computer Science Department Seminar
Major StepsNetwork Construction and TrainingNetwork Pruning
Rule extractionTraditional approaches VS NEURAL NETWORKS
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Traditional approaches VS. NEURAL NETWORKSConclusionReferences
Outline
1 Major Steps
2 Network Construction and TrainingIs Neural Network Approach Appropriate?Select Appropriate Paradigm
Select Input Data and FactsData PreparationTraining Strategies
3 Network Pruning
4 Rule extractionRule Extraction Algorithm
5 Traditional approaches VS. NEURAL NETWORKS
6 Conclusion
7 References
Computer Science Department Seminar
Major StepsNetwork Construction and TrainingNetwork Pruning
Rule extractionTraditional approaches VS NEURAL NETWORKS
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Traditional approaches VS. NEURAL NETWORKSConclusionReferences
References
Effective Data Mining Using Neural Networks by HongjunLu, Member, IEEE Computer Society, Rudy Setiono, andHuan Liu, Member, IEEE
Introduction to Data Mining Using Artificial Neural Networks,Dr. Hamid Nemati
An Introduction to Data Mining by Kurt Thearling, Ph.D.,www.thearling.com
Data Mining: Concepts and Techniques Jiawei Han andMicheline Kamber, Morgan Kaufmann, 2001.
Anderson, J. A. (2003). An introduction to neural networks,Prentice Hall.
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Major StepsNetwork Construction and TrainingNetwork Pruning
Rule extractionTraditional approaches VS NEURAL NETWORKS
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Traditional approaches VS. NEURAL NETWORKSConclusionReferences
References
http://www.mathworks.in/products/neuralnetwork/description2.html
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