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RFM is a method used for analyzing customer value and segmenting customers which is commonly used in database marketing and direct marketing. It has received particular attention in the retail and professional services industries.
Segmented regression, also known as piecewise regression or broken-stick regression, is a method in regression analysis in which the independent variable is partitioned into intervals and a separate line segment is fit to each interval.
Market segmentation is the process of dividing mass markets into groups with similar needs and wants. [2] The rationale for market segmentation is that in order to achieve competitive advantage and superior performance, firms should: "(1) identify segments of industry demand, (2) target specific segments of demand, and (3) develop specific 'marketing mixes' for each targeted market segment ...
The snakes model is popular in computer vision, and snakes are widely used in applications like object tracking, shape recognition, segmentation, edge detection and stereo matching. A snake is an energy minimizing, deformable spline influenced by constraint and image forces that pull it towards object contours and internal forces that resist ...
Reflective memory is a means to share common data between different and independent systems deterministically. [1] Such systems using a common reflective memory form a reflective memory network which is a deterministic one, when any system of the network acquired data and writes it to its local memory, such data is written locally to all other systems, this behaviour is like a dual-ported ...
Time-series segmentation is a method of time-series analysis in which an input time-series is divided into a sequence of discrete segments in order to reveal the underlying properties of its source. A typical application of time-series segmentation is in speaker diarization , in which an audio signal is partitioned into several pieces according ...
The random walker algorithm is a segmentation algorithm solving the combinatorial Dirichlet problem, adapted to image segmentation by L. Grady in 2006. [16] In 2011, C. Couprie et al. proved that when the power of the weights of the graph converge toward infinity, the cut minimizing the random walker energy is a cut by maximum spanning forest. [17]
The random walker algorithm is an algorithm for image segmentation.In the first description of the algorithm, [1] a user interactively labels a small number of pixels with known labels (called seeds), e.g., "object" and "background".