Mall Customer Segmentation Engine Through Clustering Analysis

Meenu Vijarania · Nitin Kumar · Rohit Kumar · Swati Gupta

6 citations0/0 files storedAdvances in business information systems and analytics book series

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Customer churn and segmentation Advanced Clustering Algorithms Research Data Mining and Machine Learning Applications

Abstract

Finding related information within a cluster is done using a technique called clustering. The dataset cluster uses the data's maximum and minimum values to group together similar data. Clustering is a process in which matter has been split into groups and grouped based on a rule to maximize within-group similarity and minimize between-group difference likeness. In this chapter, the authors examine and contrast the various group analysis techniques and algorithms employed by Rapid Miner. Multiple clustering methods have been developed. In the chapter, two types of clustering for algorithms are analyzed. One area of mall patrons was evaluated. The data set is used with Rapid Miner tools to determine the proper cluster.

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References

1997Forecasting the frequency of changes in quoted foreign exchange prices with the autoregressive conditional duration model 2016Customer portfolio management (CPM) for improved customer relationship management (CRM): Are your customers platinum, gold, silver, or bronze?2016Review on Customer Segmentation Technique on Ecommerce 2015Application of K-Means Algorithm for Efficient Customer Segmentation: A Strategy for Targeted Customer Services 2016Identifying the right solution customers: A managerial methodology 2018A Case Study on Customer Segmentation by using Machine Learning Methods 2024Customer Segmentation Using K-Means Algorithm

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2023Unveiling Customer Segmentation Patterns in Credit Card Data using K-Means Clustering: A Machine Learning Approach

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