seminar slide.pptx
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Nur Ira Amira Binti Md NorWET110022
Lecturer: Dr Shivakumara Palaiahnakote
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The task is to learn to assign instances topredefined classes.
Requires supervised learning: the trainingdata has to specify what we are trying tolearn (the classes)
Classifier is a mathematical function,implemented by a classificationalgorithm, that maps input data to acategory which performs classification
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Two common approaches:
Probabilistic
Geometric Useful for many search-related tasks
Spam detection
Sentiment classification Online advertising
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A tree structured prediction model where
each internal node denotes a test on an
attribute, each outgoing branch represents
an outcome of the test and each leaf node
is labeled with a class or class distribution.
Attribute to be predicted: dependent
variable Attribute that help in predicting dependent
variable: independent variable
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Figure below shows a decision tree withtests on attributes X and Y:
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Consider that the captain of a cricketteam has to decide whether to bat or
field first in the event that they win thetoss.
He decides to collect the statistic of thelast ten matches when the winningcaptain has decided to bat first andcompare in order to decide what to do.
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INDEPENDENT VARIABLES DEPENDENTVARIABLE
Outlook Humidity No of batsmenin team > 6
Final outcome
Sunny High Yes Won
Overcast High No Lost
Sunny Low No Lost
Sunny High No Won
Overcast Low Yes Lost
Sunny Low Yes Won
Sunny Low No Lost
Sunny High No Won
Sunny Low Yes Won
Sunny Low Yes Won
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Dependent variable: game won or lost
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Works on a simple, but comparatively
intuitive concept.
It makes use of the variables contained inthe data sample, by observing them
individually, independent of each other.
Based on the Bayes rule of conditional
probability. It makes use of all the attributescontained in the data, and analyses them
individually as though they are equally
important and independent of each other.
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Consider that the training data consists of
various animals (say elephants, monkeys
and giraffes), and our classifier has to
classify any new instance that it encounters.
We know that elephants have attributes like
they have a trunk, huge tusks, a short tail,
are extremely big, etc. Monkeys are short insize, jump around a lot, and can climb
trees; whereas giraffes are tall, have a long
neck and short ears.
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The Nave Bayes classifier will consider each ofthese attributes separately when classifying anew instance.
When checking to see if the new instance is anelephant, the Nave Bayes classifier will notcheck whether it has a trunk and has hugetusks and is large. Rather, it will separately
check whether the new instance has a trunk,whether it has tusks, whether it is large, etc. Itworks under the assumption that one attributeworks independently of the other attributescontained by the sample
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The task is to learn a classification fromthe data. No predefined classification isrequired.
An unsupervised learningthe trainingdata doesnt specify what we are tryingto learn (the clusters)
Clustering algorithms divide a data setinto natural groups (clusters).
Often use a distance measure fordissimilarity
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General outline of clustering algorithms1. Decide how items will be represented (e.g.,
feature vectors)
2. Define similarity measure between pairs orgroups of items (e.g., cosine similarity)3. Determine what makes a good clustering4. Iteratively construct clusters that are
increasingly good
5. Stop after a local/global optimum clustering isfound
Steps 3 and 4 differ the most acrossalgorithms
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Segment customer database based onsimilar buying patterns
Group houses in a town intoneighborhood based on similar features
Identify similar Web usage patterns
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Hierarchical Clustering Has two versions:
Agglomerative (bottom up) Divisive (top down)
Overlapping Clustering Uses fuzzy sets to cluster data, so that each point
may belong to two or more clusters with differentdegrees of membership.
Exclusive clustering
Data are grouped in exclusive way, so that a certaindatum belongs to only one definite cluster.
Eg: K-means clustering
Probabilistic Clustering Uses a completely probabilistic approach.
Eg: Mixture of Gaussian
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Hierarchy can be visualized as aDendogram - a tree data structure
which illustrates hierarchical clusteringtechniques.
Each level shows clusters for that level
Leafindividual clusters
Rootone cluster
A cluster at level i is the union of itschildren clusters at level i+1
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A D EB C F G
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Divisive Initially all items in one cluster
Large clusters are successively divided
Top Down
Agglomerative Initially each item in its own cluster
Iteratively clusters are merged together
Bottom Up
How do we know how to divide or combinedclusters? Define a division or combination cost
Perform the division or combination with the lowestcost
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F
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F
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Single Linkage
Smallest distance between points
Complete Linkage Largest distance between points
Average Linkage
Average distance between points Average Group Linkage
Distance between centroids
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F
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Single Linkage CompleteLinkage
AverageLinkage
Average GroupLinkage
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One of the simplest unsupervisedlearning algorithms that solves the
clustering problem. K-means always maintains exactly K
clusters
Clusters represented as centroids (center of
mass)
The main idea is to define K centroids,one for each cluster.
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Basic algorithm:
Step 1: Choose Kcluster centroids
Step 2: Assign points to closet centroid Step 3: Recompute cluster centroids
Step 4: Goto 2
Tends to converge quickly
Can be sensitive to choice of initialcentroids
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