So Far…… Clustering basics, necessity for clustering, Usage in various fields : engineering and industrial fields Properties : hierarchical, flat, iterative, hard, soft, disjunctive Types: Supervised and unsupervised K-means algorithm – for unsupervised clustering Vector quantization – for supervised clustering CONTINUE with fuzzy and neural net algorithms – supervised clustering
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So Far…… Clustering basics, necessity for clustering, Usage in various fields : engineering and industrial fields Properties : hierarchical, flat,
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So Far……
Clustering basics, necessity for clustering, Usage in various fields :
For the incoming data, calculate the degree of membership to all the
centers using the following formula
On-line phase
C
j
m
jk
ik
ki
d
d
u
1
1
2
,
,
,
1
22, )( ikik vxd
Ci .....2,1
kiu ,
ikd ,
- fuzzy membership of the k-th point to the i-th cluster
- Euclidean distance between the data point and the cluster center
Artificial Neural Net systems
Based on low level microscopic biological models
Originated from modeling of human brain and evolution
Collective behavior of NN, like a human brain, demonstrates the
ability to learn, recall and generalize from training patterns of data
Consists of large number of highly interconnected processing
elements (nodes)
Application areas: speech recognition, speech to text conversion,
image processing, investing, trading……
Model specified by three basic elements:
Models of the processing element
Models of interconnections and structures (network topology)
Learning rules (the way information is stored in the network)
Each node collects values from all its input connections, performs a
predefined mathematical operation and produces a single output
Net input to the node = integration function (typically dot product)
combining all inputs
Each input connection is weighed. These weights (can be positive or
negative values) are determined during learning process. Because
of this adjustments, NN is able to learn.
Activation output of node = activation function ( net input ), usually
a non-linear function
Ex: Activation function Unipolar ( output takes the value 0 or 1 )
Output y = 0 , if sum < threshold (b)
= 1 , if sum > threshold (b)
sum is dot product of input vector x= ( ) and weight vector w = ( )
N
nnn xws
1
Nxx ...............1
Nww .................1
Some activation functions (unipolar and bipolar)
Input vector x = ( ) Linearly combined with weights
Then s is activated by a threshold function T(-) to produce the output y = T(s) = 1 when s > 0, else y = T(s) = -1.
Then all the input vectors x such that
forms a Hyperplane H in the input vector space. H partitions the feature vector space into right and left half spaces, H+ (when sum is > b) and H- (when sum < b)
The Perceptron as Hyperplane separator
bxwxwS NN .........11 , where b is the threshold
0.........11 bxwxwSx NN
Nxx ...............1
Ex : consider a single perceptron with two inputs
Let w1 = 2 andw2 = -1, b=0, then 2x1 - x2 = 0 determines H
the points (0,0) and (1,2) belong to H
The feature vector x = (x1,x2) = (2,3) is summed into
S = 2(2) - 1(3) = 1 > 0, so that the activated output is y = T(1) = 1
(corresponds to H+ in the plane, i.e right half)
(x1,x2) = (0,2) activates the output y = T(2(0) - 1(2)) = T(-1) = -1,
which indicates that (0,2) is in the left halfspace H-. The figure shows
these points.
Mapping in Hyperplane. (example of linear mapping between input and output
Non-linear mapping between input and output
Example : XOR logic function or 2- bit parity problem.
N = 2 inputs, M = 1 output, and Q = 4 sample vector (input/output)
pairs for training, and K= 2 clusters (even and odd).
Hyperplane diagram for 2-bit parity problem
XOR function implementation in three layered network
Take:
result is two parallel hyperplanes that yield three convex regions. The
hyperplanes are determined by
The threshold at the first neuron in the hidden layer yields
The threshold at the second hidden neuron yields
hyperplanes yield three convex regions. The four sets of above inputs
yield the three unique vectors
Corresponding to three regions in the hyperplane
Hyperplanes showing three regions for 2-bit parity problem
Regions 1 and 3make up the odd parity (Class 2),while Region 3 is even parity (Class 1).
For the second layer (output layer), the equations are as follows: