Classification and Cluster 2BCasic Concepts

Classification and Cluster 2BCasic Concepts

1. Classification and
Cluster

2. Basic Concepts of Classification and
Clustering
Classification
Definition:
Classification is the process of predicting the category or class to which
a new data point belongs, based on the knowledge from a training
dataset with known labels.
Example:
Given a dataset of email messages labeled as "spam" or "not spam," a
classification algorithm can predict the label of new email messages.
•

3. Clustering
Definition:
Clustering is the process of grouping data points into clusters
(groups) based on similarity, where similar data points are
placed in the same cluster and dissimilar ones in different
clusters. No predefined labels are used.
Example:
In a customer dataset, clustering can group customers with
similar purchasing behavior into different clusters, such as
high spenders and low spenders.
•

4. Decision Tree Induction
•Decision tree induction is a method used in machine learning
for creating a decision tree based on a dataset. It's a popular
supervised learning technique used for both classification and
regression tasks. In simple terms, a decision tree is a
flowchart-like structure where:
•Each internal node represents a decision based on a feature
(attribute).
•Each branch represents the outcome of the decision (a feature
value).
•Each leaf node represents a class label or continuous value .
•

5. Bayes Classification Method
•Bayes classification is a probabilistic classification
technique based on Bayes' Theorem, which describes the
probability of a class label given certain features
(predictors). It is widely used in machine learning and
statistics for both classification and prediction tasks.
•Bayes' Theorem allows us to calculate the probability of a
class label C given observed features X

6. Bayes' Theorem
•Bayes' Theorem is a fundamental concept in probability
theory that describes the relationship between conditional
probabilities. It provides a way to update the probability of
an event based on new evidence. Named after the
statistician Thomas Bayes, this theorem is widely used in
various fields, such as statistics, machine learning, and
artificial intelligence.
•
•Formula for Bayes' Theorem:
P(C X)=P(X C)P(C)/P(X) 
∣ ∣

7. Rule-Based Classification
•Rule-based classification is a type of classification model
where the data is classified based on a set of if-then rules.
•
•These rules are used to make decisions about the class or
category of a given data point based on its features.
•
•Rule-based classifiers are intuitive, easy to understand, and
often used for expert systems, decision support systems,
and situations requiring transparency.
•

8. Cluster Basic Concept and Methods:
•Clustering is an unsupervised learning technique used in
data mining and machine learning to group similar data
points into clusters. The objective is to divide a dataset into
subsets (clusters) where each data point within a cluster is
more similar to each other than to data points in other
clusters. Clustering is used for exploratory data analysis,
pattern recognition, and finding natural groupings in data.

9. Cluster Analysis
•Cluster analysis (also known as clustering) is a method of
unsupervised learning that aims to group a set of objects
(data points) into clusters, where each cluster contains
objects that are similar to each other, and dissimilar to
objects in other clusters. The goal is to discover inherent
structures within the data without any prior knowledge
about class labels.
•Cluster analysis is widely used in various fields like data
mining, machine learning, pattern recognition, and
statistics. It helps in uncovering patterns, similarities, and
groupings in data, especially when labels are not available.
•