Artificial intelligence (AI) refers to the simulation of human intelligence in machines that are programmed to think like humans and mimic their actions. The term may also be applied to any machine that exhibits traits associated with a human mind such as learning and problem-solving.

1. DEPARMENT OF COMPUTER SCIENCE
AND INFORMATION TECHNOLOGY
presented by
NIBIYA.G
I-Msc(IT)

2.  SEMINAR TOPIC:
 Support vector machine algorithm
 Classification of association rules

3.  Support Vector Machine or SVM is one of the
most popular Supervised Learning
algorithms, which is used for Classification as
well as Regression problems.
 However, primarily, it is used for
Classification problems in Machine Learning.

4.  The goal of the SVM algorithm is to create the
best line or decision boundary that can
segregate n-dimensional space into classes
so that we can easily put the new data point
in the correct category in the future.
 This best decision boundary is called a
hyperplane.
 These extreme cases are called as support
vectors, and hence algorithm is termed as
Support Vector Machine.

6.  SVM can be understood with the example that
we have used in the KNN classifier.
 Suppose we see a strange cat that also has
some features of dogs, so if we want a model
that can accurately identify whether it is a cat
or dog, so such a model can be created by
using the SVM algorithm.

8.  SVM can be of two types:
◦ Linear SVM
◦ Non-linear SVM
 Linear SVM: Linear SVM is used for linearly separable
data, which means if a dataset can be classified into
two classes by using a single straight line, then such
data is termed as linearly separable data, and classifier
is used called as Linear SVM classifier.

9.  Non-linear SVM: Non-Linear SVM is used for non-
linearly separated data, which means if a dataset
cannot be classified by using a straight line, then
such data is termed as non-linear data and
classifier used is called as Non-linear SVM
classifier.

10.  Analyzes and predicts customer behaviour
if/then statement.
 EXAMPLE:
 Bread=>butter
 Buys{onions,potatoes}=>buys(tomatoes)

11. Bread=>butter{20%,45%}
 Bread : Ancedent
 Butter : consequent
 20% : support
 45% : confidence

12. A=>B
 Support denotes probability that contains
both A&A
 Confidence denoted probability that a
transaction containing A also contain B

13.  Consider, in a supermarket
◦ Total transactions:100
◦ Bread:20
So, 20/100*100=20% which is support
In 20 transaction, butter:9 transaction
So, 9/20*100=45% which is confidence

14.  Single dimensional rules
Bread->butter
 Multi-dimensional rules
With 2 or more predicates or dimensional
occupation (I,T),age(>22) =>buys(laptop)
 Hybrid dimensional rules
With repetative predicates or dimensional time
(5’o clock),buys(tea)=>buys(biscuits)

15.  Web usage mining
 Banking
 Bio-Inforamtion
 Market based analysis
 Credit/debit card analysis
 Product clustering
 Catalog design

16.  Apriori algorithm
 Elcat algorithm
 F.P growth algorithm