نتایج جستجو برای: customer clustering analysis
تعداد نتایج: 2896338 فیلتر نتایج به سال:
Classification and patterns extraction from customer data is very important for business support and decision making. Timely identification of newly emerging trends is very important in business process. Large companies are having huge volume of data but starving for knowledge. To overcome the organization current issue, the new breed of technique is required that has intelligence and capabilit...
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 ...
In some cases, customer classi$cation is important for the development of advanced logistical distribution strategies in response to the growing complexity in business logistical markets. This paper presents a new approach that can be employed to cluster customers before executing 5eet routing in logistical operations. The proposed approach is developed on the basis of fuzzy clustering techniqu...
The aim of this study is to research the possibility of using customer transactional data to identify spending patterns among individuals, that in turn can be used to assess creditworthiness. Two different approaches to unsupervised clustering are used and compared in the study, one being K-means and the other an hierarchical approach. The features used in both clustering techniques are extract...
Telecommunication sector generates a huge amount of data due to increasing number of subscribers, rapidly renewable technologies; data based applications and other value added service. This data can be usefully mined for churn analysis and prediction. Significant research had been undertaken by researchers worldwide to understand the data mining practices that can be used for predicting custome...
A fuzzy clustering based solution to the CoIL Challenge 2000 is described. The challenge consists of correctly determining which customers have caravans in a real world customer data base, and of describing the characteristics of their profile. The solution provided uses fuzzy clustering to granulate different features and determines a score for each cluster. A version of the fuzzy c-means algo...
Aiming to large-scale Multiple-Depot Food transport Vehicle Routing Problem (MDFVRP), this study proposed an improved genetic algorithm solution frame based on the two-stage fuzzy clustering. In the static upper stage, the k-means technology is used to divide the MDFVRP into several one-to-many sub-problems. From the perspective of improving the customer satisfaction and integrating logistics r...
Despite the importance of data mining techniques to customer relationship management (CRM), there is a lack of a comprehensive literature review and a classification scheme for it. This is the first identifiable academic literature review of the application of data mining techniques to CRM. It provides an academic database of literature between the period of 2000–2006 covering 24 journals and p...
14:00-14:45 " Network-based auto-probit modeling with application to protein function prediction " Eric D. Kolaczyk 09:35-10:10 " Quantifying and comparing complexity of cellular networks: structure beyond degree statistics " Alessia Annibale and Anthony Coolen 10:10-10:45 " Node and link roles in protein-protein interaction networks " " Using Distinct Aspects of Social Network Analysis to Impr...
Companies’ managers are very enthusiastic to extract the hidden and valuable knowledge from their organization data. Data mining is a new and well-known technique, which can be implemented on customers data and discover the hidden knowledge and information from customers' behaviors. Organizations use data mining to improve their customer relationship management processes. In this paper R, F, an...
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