The rational function model (RFM) is a generic form of many sensor models. The universal real-time image geometry model recommended by the OpenGIS consortium is essentially based on the RFM. Many data vendors are or will be supporting the RFM based solutions by providing rational function coefficients (RFCs). With known RFCs, the end users will be able to rectify the imagery using the RFM witho...

The physical and generalized sensor models are two widely used imaging geometry models in the photogrammetry and remote sensing. Utilizing the rational function model (RFM) to replace physical sensor models in photogrammetric mapping is becoming a standard way for economical and fast mapping from high-resolution images. The RFM is accepted for imagery exploitation since high accuracies have bee...

This study aimed at providing a systematic method to analyze the characteristics of customers’ purchasing behavior in order to improve the performance of customer relationship management system. For this purpose, the improved model of LRFM (including Length, Recency, Frequency, and Monetary indices) was utilized which is now a more common model than the basic RFM model apt for analyzing the cus...

In recent years, customer lifetime value (CLV) has gained increasing importance in both academia and practice. While many advanced techniques have been proposed, the recency/frequency/monetary value (RFM) segmentation framework, and its related probability models, remain a CLV mainstay. In this article, we demonstrate the de_ciency in RFM as a basis for summarizing customer history (data compre...

RFM model is an important method in customer clustering. Some past studies have proposed fuzzy RFMs model to overcome the shortcomings of traditional RFM models. However, there are some problems unsolved in these approaches. To deal with these problems and to enhance the flexibility in customer clustering, the traditional fuzzy c-means (FCM) method is fuzzified to deal with R, F, and M scores t...

RFM and CLV: Using Iso-value Curves for Customer Base Analysis We present a new model that links the well-known RFM (recency, frequency, monetary value) paradigm with customer lifetime value (CLV). While previous researchers have connected the two conceptually, none has presented a formal model that requires nothing more than RFM inputs to make specific lifetime value projections for a set of c...

In this paper, we present a new technique for statistical modeling of speech segments based on Markov random fields. Classical and multi-stream HMMs are particular cases of this more general family of models. However, the Random Field Model (RFM) proposed here can be seen as an extension of the multiband HMM in which interactions between the frequency bands have been added. In a first experimen...

Numerous firms accumulate large quantities of data or transactions after importing information systems and services, which leads to troubles with procedure. Firms also have demands find customers’ from datasets understand how develop marketing strategies accurately adjust their operational methods. Therefore, this study proposed customer ranking combined Big Data process based on the RFM model ...

Data mining is a well-known technique, which can be used to extract hidden information about customers’ behaviors. It is used to improve the customer relationship management processes by various Organizations. Previous researches in constraint based pattern mining emphasis only on the concept of frequency.But the changes in the environment may occur frequently, so the frequently occuring patter...

High-resolution stereo satellite imagery is widely used in environmental monitoring, topographic mapping, and urban three-dimensional (3D) reconstruction. However, a critical issue in these applications using high-resolution stereo satellite imagery is to improve the accuracy of point geo-positioning. This paper presents a framework for comparison of the performance of the three-dimensional (3D...