Easy Flashcards
Distance Functions, Euclidean, _, _
Manhattan, Minkowski
optimal dataset for K neighbours
3-10
association rules find all sets of itemsets that have _ greater than the minimum
support
association rules: find desired rules that have _ greater than the min
confidence
association rules are usually needed to satisfy a user-specified _ & _
minimum support, confidence
formula: support for association rules
frq(x,y)/n
formula: confidence for association rules
frq(x,y)/frq(x)
K-means clustering… place _ at _ locations; repeat until convergence
centroids, random
K-means clustering… 1. for each point xi:
find nearest centroid
K-means clustering… 2. assign the point, & for each determine new centroid
to cluster
K-means clustering… 3. stop when non of the __ change
clustering assignments
a perceptron is used to classify _ classes
linearly separable
a percepton consists of _, _, _
weights, summation processor, activation function
a percepton takes a weighted sum of input and outputs, 1 if the sum > than _ _ _ _, _
some adjusted threshold value, theta
the perceptron can have another input known as
the bias
perceptron: it is normal practice to treat the bias as
just another input
the perceptron bias allows us to
shift the transfer curve horizontally along the input axis
the perceptron weights determine
the slope of the curve
draw the perceptron
perceptron concept: the ouput is set at one of two levels, depending on whether the _, is greater or less than some _ value. This is called:
total input, threshold, unit step (threshold)
draw the unit step threshold
Perceptron function: the _ consists of two functions, _ and _ , ranging from 0 and 1, and -1 to +1
sigmoid, logistic, tangential
perceptron function: Ouput is proportional to the total weighted output
piecewise linear
perceptron function: bell shaped curves that are continuos. the node output (high / low) is interpreted in terms of class membership (1/0) depending on how close the net input is to a _
Gaussian, chosen value of average
