Hybrid Model for Pattern Discovery in Data Communication to Enhance Customer Relationship Management using Data Mining Techiques

• Published in: Journal of physics. Conference series Vol. 1979; no. 1; pp. 12048 - 12055
• Main Authors: Arivazhagan, B, Sankara Subramanian, R
• Format: Journal Article
• Language: English
• Published: Bristol IOP Publishing 01.08.2021
• Subjects: Algorithms; Clustering; Communication; Customer relationship management; Customer satisfaction; Customer services; Customers; Data communication; Data mining; Discounts; Evaluation; Scientific papers
• ISSN: 1742-6588; 1742-6596
• DOI: 10.1088/1742-6596/1979/1/012048

Abstract – Data Communication in customer oriented sector has significant information which is considered as a vital part in enhancing and improving customer base. Loyal customers in business sector are based on buying and selling of products or making use of services provided. Generally, the customers may switch on to variant enterpriser who affords best services with best price hence retaining them is a tedious task unless their communiaction data regarding service is analysed in periodic and to keep track of customer, the enterpriser should know the purchasing pattern and needs of loyal customersThe discovered pattern is also helps to afford proper discounts to the customers at the right time. This research paper experiments customer data in Telecommunication sector to discover useful and interesting patterns. There are so many data mining techniques put forward in revealing the pattern and this paper discusses all the possible mining techniques and evaluates an association rule mining with clustered data to create best rules. For clustering, Hierarchical agglomerative is used and FP-Growth algorithm is used for association rule mining. Both clustered data and association rules results regarding data communication are presented. The research work is implemented in weka tool and assessed with suitable evaluation metrics.