 |
International Journal for Research Trends and Innovation
International Peer Reviewed & Refereed Journals, Open Access Journal
ISSN Approved Journal No: 2456-3315 | Impact factor: 8.14 | ESTD Year: 2016
Scholarly open access journals, Peer-reviewed, and Refereed Journals, Impact factor 8.14 (Calculate by google scholar and Semantic Scholar | AI-Powered Research Tool) , Multidisciplinary, Monthly, Indexing in all major database & Metadata, Citation Generator, Digital Object Identifier(DOI)
|
Issue: December 2025
Volume 10 | Issue 12
Review Result and Publication of Paper within : 2-3 days
Click Here For more DetailsFor Authors
Forms / Download
Published Issue Details
Editorial Board
Other IMP Links
Facts & Figure
Impact Factor : 8.14
Issue per Year : 12
Volume Published : 10
Issue Published : 115
Article Submitted : 19463
Article Published : 8041
Total Authors : 21252
Total Reviewer : 769
Total Countries : 145
Indexing Partner
Licence
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License
|
Published Paper Details
|
|
| Paper Title: | CUSTOMER SEGMENTATION USING AGGLOMERATIVE CLUSTERING |
| Authors Name: | Dr. M. Rajasekaran , DAMA KRISHNAMOHAN , CHITRALA GOWTHAM SAI , CHUNDURI CHETAN |
| Download E-Certificate: | Download |
| Author Reg. ID: |
IJRTI_202099
|
| Published Paper Id: | IJRTI2504078 |
| Published In: | Volume 10 Issue 4, April-2025 |
| DOI: | |
| Abstract: | Customer segmentation plays a crucial role in understanding consumer behavior, enabling businesses to tailor their strategies to meet specific needs and preferences. This paper explores the implementation of customer segmentation using Agglomerative Clustering, a hierarchical clustering method that groups data points based on their similarity in a bottom-up approach, forming a dendrogram to visualize the merging of clusters. By leveraging demographic and behavioral attributes such as gender, marital status, age, education level, profession, work experience, spending score, and family size, this study categorizes customers into distinct clusters, providing actionable insights for targeted marketing and personalized services. The clustering process starts by treating each data point as an individual cluster and iteratively merges the closest clusters until a predefined number of clusters remain. Agglomerative Clustering is particularly advantageous for its flexibility in accommodating varied data distributions and its ability to uncover hierarchical relationships within the dataset. To enhance accessibility and real-time interaction, the research integrates the clustering model with Gradio, a Python library designed to create user-friendly web interfaces for machine learning applications. Through the Gradio interface, users can input customer attributes directly and receive immediate cluster predictions, simplifying the deployment and usability of the model. The system not only predicts cluster membership but also provides an interpretative framework for understanding customer groups, which are labeled based on their shared characteristics to align with marketing strategies. For instance, clusters might be categorized as high-spending professionals, budget-conscious families, or young aspirational individuals, each requiring tailored engagement approaches. The model ensures accurate clustering by using the distance matrix to measure the similarity between input data and computed cluster centers, minimizing the error in classification. This integration of Agglomerative Clustering and Gradio demonstrates how machine learning and interactive tools can work synergistically to enhance decision-making processes in business. |
| Keywords: | |
| Cite Article: | "CUSTOMER SEGMENTATION USING AGGLOMERATIVE CLUSTERING ", International Journal of Science & Engineering Development Research (www.ijrti.org), ISSN:2455-2631, Vol.10, Issue 4, page no.a581-a590, April-2025, Available :http://www.ijrti.org/papers/IJRTI2504078.pdf |
| Downloads: | 000373 |
| ISSN: |
2456-3315 | IMPACT FACTOR: 8.14 Calculated By Google Scholar| ESTD YEAR: 2016 An International Scholarly Open Access Journal, Peer-Reviewed, Refereed Journal Impact Factor 8.14 Calculate by Google Scholar and Semantic Scholar | AI-Powered Research Tool, Multidisciplinary, Monthly, Multilanguage Journal Indexing in All Major Database & Metadata, Citation Generator |
| Publication Details: |
Published Paper ID: IJRTI2504078
Registration ID:202099
Published In: Volume 10 Issue 4, April-2025
DOI (Digital Object Identifier):
Page No: a581-a590 Country: 4433, tamil nadu, India Research Area: Engineering Publisher : IJ Publication Published Paper URL : https://www.ijrti.org/viewpaperforall?paper=IJRTI2504078 Published Paper PDF: https://www.ijrti.org/papers/IJRTI2504078 |
| Share Article: | |
|
Click Here to Download This Article |
|
| Article Preview | |
|
|
|
Major Indexing from www.ijrti.org
| Google Scholar | ResearcherID Thomson Reuters | Mendeley : reference manager | Academia.edu |
| arXiv.org : cornell university library | Research Gate | CiteSeerX | DOAJ : Directory of Open Access Journals |
| DRJI | Index Copernicus International | Scribd | DocStoc |
ISSN Details
 |
 |
|
ISSN: 2456-3315
Impact Factor: 8.14 and ISSN APPROVED,
Journal Starting Year (ESTD) : 2016
DOI (A digital object identifier)
| Providing A digital object identifier by DOI.ONE How to Get DOI? |
Conference
Open Access License Policy
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License
Important Details
Join RMS/Earn 300
WhatsApp
Click Here
Click Here
Indexing Partner |
|||
| Copyright © 2025 - All Rights Reserved - IJRTI | |||