Optimizing Web Search Results for Image. K-means Clustering Algorithm


Academic Paper, 2020

55 Pages, Grade: 9.5


Excerpt


TABLE OF CONTENTS

Chapter 1: Introduction
1.1 Clustering
1.2 Types of Clustering
1.3 Classification of Clustering Algorithms
1.4 Requirements of Clustering
1.5 Stages in Clustering
1.6 Different Types of Clusters
1.7 Different Types of Clustering Algorithms
1.8 Applications of Clustering
1.9 Web Clustering Engines

Chapter 2: Literature Survey

Chapter 3: Tools and Technologies
3.1 System Requirements
3.2 System Environment

Chapter 4: Problem Description
4.1 Existing System
4.2 Objective
4.2.1 HACM Clustering Algorithm and its Shortcomings
4.2.2 K-Means Clustering Algorithm and its Advantages over HACM
4.3 Proposed System

Chapter 5: System Design
5.1 System Architecture

Chapter 6: Conclusion and Future Work

References

Excerpt out of 55 pages

Details

Title
Optimizing Web Search Results for Image. K-means Clustering Algorithm
Grade
9.5
Author
Year
2020
Pages
55
Catalog Number
V983236
ISBN (eBook)
9783346348586
ISBN (Book)
9783346348593
Language
English
Keywords
optimizing, search, results, image, k-means, clustering, algorithm
Quote paper
Priyanka Nandal (Author), 2020, Optimizing Web Search Results for Image. K-means Clustering Algorithm, Munich, GRIN Verlag, https://www.grin.com/document/983236

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