Data Mining And Ai Algorithms

Without any Tool

What you'll learn

Mining Association Rules

Clustering Technique

Classification Technique

Simulation

Applications

Requirements

Basic Mathematics

Description

This course discusses some of the most important Data Mining Techniques without use of any programming language or tool. These techniques provide the foundation of intelligent system. The focus is on learning the concepts in simple way and understanding how these techniques can change the way we do our work. This knowledge is necessary to be able to take advantage of AI implementation in the organization.This course focusses on conceptual discussions and tries to go deep into the concepts. Small datasets have been used so that one can do the computations manually and get the feel of various algorithms. We have discussed some fine points of Association Mining and Clustering here. Few questions related to Simulation too have been discussed without having much discussion about the techniques. As we are building this course, we are giving priority to some immediate requirements.We first aim to do the three important data mining techniques – Clustering, Association and Classification in detail. This course is being developed. At present, it is meeting partial requirement of a set of students in a particular course. If you are not from that course and enrol here, you get the feel of what we aim at. We will built it over coming few months.You can ask questions. Or, if there is any urgent need, please message me through the system. Those needs can be taken care on priority.

Overview

Section 1: Data Mining - Going Deep up to Hidden Valuable Patterns

Lecture 1 Hidden Valuable Pattern

Section 2: Mining Association Rules

Lecture 2 Association Rules - Apriori Algorithm

Lecture 3 Threshold Probability and Support

Lecture 4 Revision Questions Set A with Solutions

Section 3: Clustering

Lecture 5 K-Means Clustering

Lecture 6 Change in Cluster – An Example

Lecture 7 Practice Question Set A with Solution

Section 4: Exercises

Lecture 8 Association Rules Exercise

Lecture 9 K-Means Clustering Exercise

Section 5: Simulation

Lecture 10 Understanding Uncertainty and Probability

Lecture 11 Discrete Variable Simulation using Random Number Table

Lecture 12 Few Questions with Solutions

Section 6: Examination Questions with Answers

Lecture 13 Simulation Question

Lecture 14 Clustering Question

Lecture 15 Association Question

Students and Professionals looking for conceptual discussions on Data Mining