A Two Phase Clustering Method for Intelligent Customer Segmentation

Namvar, Morteza; Gholamian, Mohammad Reza; KhakAbi, Sahand

doi:10.1109/isms.2010.48

Cited by 61 publications

(25 citation statements)

References 16 publications

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“…[26] also implemented K-Means for customer segmentation on their dataset. Although, hierarchical clustering algorithm seems unsuitable to many, [27] have used it for intelligent customer segmentation for their research and [28] have made use of it for applying clustering algorithms on the transaction data from a supermarket. K-means and Hierarchical Clustering algorithms are useful for clustering data and find extensive usage in customer segmentation.…”

Section: A Clustering For Segmentation Purposesmentioning

confidence: 99%

Approaches to Clustering in Customer Segmentation

Tripathi¹,

Bhardwaj²,

Poovammal³

2018

IJET

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Customer Relationship Management(CRM) has always played a crucial role as a market strategy for providing organizations with the quintessential business intelligence for building, managing and developing valuable long-term customer relationships. A number of business enterprises have come to realize the significance of CRM and the application of technical expertise to achieve competitive advantage. This study explores the importance of Customer Segmentation as a core function of CRM as well as the various models for segmenting customers using clustering techniques. The available clustering models for customer segmentation, in general, and the major models of K-Means and Hierarchical Clustering, in particular, are studied and the virtues and vices of the techniques are pointed out. Finally, the possibility of developing a hybrid solution by the combination of the above two techniques, having the ability to outperform the individual models, is discussed.

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Section: A Clustering For Segmentation Purposesmentioning

confidence: 99%

Approaches to Clustering in Customer Segmentation

Tripathi¹,

Bhardwaj²,

Poovammal³

2018

IJET

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show abstract

“…This results in producing clusters of similar types of data, as described in [1]. Further refine in this process done by performing clustering operation on the already clustered data, which provides us a further degree of refinement [10]. After the successful completion of this process, the acquired clusters are analyzed for common information.…”

Section: The Customer Relationship Management Processmentioning

confidence: 99%

“…A second phase Clustering is conceded out on each of these obtained clusters as defined in [10]. These clusters will correspond to a new property provided by the customer.…”

Section: Fig 5: User Profile Evaluation (Process Of Clustering)mentioning

confidence: 99%

Enhancing Customer Relationship Management by Integrating Customer and Product Values based on Clustering and Indexing Techniques

Jayaraj¹,

Rajakumar²,

Lavanya³

et al. 2015

IJCA

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Customer relationship management is not only pure business, but also indicates strong personal bonding within people. Development of this type of bonding drives the business to new levels of success. Once this personal and emotional linkage is built, it is very easy for any organization to identify the actual needs of the customer and to help and serve them in a better way. It is a belief that if more sophisticated strategies are involved in implementing the customer relationship management, the business becomes stronger and fruitful.The main objective of this paper is to provide an optimal solution which optimizes the process of Customer Relationship Management. The process is primarily executed by means of the Clustering technique .When a new customer arrives, that customer is classified into any one of the existing Clusters which automatically provides us the properties of the customer. This paper presents two phases of Clustering method which is not only the clusters on the base of an only attribute, but performs a secondary clustering operation for improved and precise results. Additionally all the Clustered information is indexed in such a manner that Clustering come to be faster.

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“…Real life areas like speech recognition, genome data analysis and ecosystem data analysis also analysis of geographical information systems [3] [1]. Data clustering is used regularly in many applications such as data mining, vector quantization, pattern recognition, and fault detection & speaker recognition [5].…”

Section: Introductionmentioning

confidence: 99%

A Novel Approach for Data Clustering using Improved K-means Algorithm

Suryawanshi¹,

Puthran²

2016

IJCA

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The dangers phishing becomes considerably bigger problem in online networking, for example, Facebook, twitter and Google+. The phishing is normally completed by email mocking or texting and it frequently guides client to enter points of interest at a phony sites whose look and feel are practically indistinguishable to the honest to goodness. Non-technical user resists learning of anti-phishing tech-nic. Also not permanently remember phishing learning. Software solutions such as authentication and security warnings are still depending on end user action. In this paper we are mainly focus on a novel approach of real time phishing email classification using K-means algorithm. For this we uses 160 emails of last year computer engineering students. we get True positive of legitimate and phishing as 67% and 80% and true negative is 30 % and 20%.,which is very high so we ask same users reasons which I mainly categories into three categories ,look and feel of email, email technical parameters, and email structure.

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A Two Phase Clustering Method for Intelligent Customer Segmentation

Cited by 61 publications

References 16 publications

Approaches to Clustering in Customer Segmentation

Approaches to Clustering in Customer Segmentation

Enhancing Customer Relationship Management by Integrating Customer and Product Values based on Clustering and Indexing Techniques

A Novel Approach for Data Clustering using Improved K-means Algorithm

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