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Abstract
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Article Information:
Deployment of Partitioning Around Medoids Clustering Algorithm on a Set of Objects Derived from Analytical CRM Data
J. Mbarki and E.M. Jaara
Corresponding Author: J. Mbarki
Submitted: April 06, 2013
Accepted: June 25, 2013
Published: January 27, 2014 |
Abstract:
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The aim of this study is to highlight the importance of the unsupervised learning in Data mining and CRM fields. Data mining commonly known by its acronym KDD: knowledge discovery in data base, it refers to all methods and algorithms used for data exploration or prediction in large data bases volumes, Data mining is very important in various fields such as science, business and other areas deal with a large data set. CRM: Customer Relationship Management is an integrated information system that is used to plan, schedule and control the pre-sales and post-sales activities in an organization, both CRM and data mining techniques helps organizations maximize the value of every customer interaction and drive superior corporate performance. Clustering is one of the favoured used methods in data mining: The objective of this study is to implement the clustering algorithm K-Medoids via a shell script applied on a set of Analytical CRM data stored in Teradata environment.
Key words: Clustering, CRM, data set, database, teradata environment, ,
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Cite this Reference:
J. Mbarki and E.M. Jaara, . Deployment of Partitioning Around Medoids Clustering Algorithm on a Set of Objects Derived from Analytical CRM Data. Research Journal of Applied Sciences, Engineering and Technology, (4): 786-790.
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ISSN (Online): 2040-7467
ISSN (Print): 2040-7459 |
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