Customer segmentation is a prerequisite to all three phases of customer relationship management which consists of customer acquisition, customer retention and customer development. Input variables which are used in clustering techniques determine which phase of customer relationship management it is dealing with. As a result this paper aims at a review on the input variables used in customer se...

Process Mining is a technology for extracting non-trivial and useful information from execution logs. For example, there are many process mining techniques to automatically discover a process model describing the causal dependencies between activities. Unfortunately, the quality of a discovered process model strongly depends on the quality and suitability of the input data. For example, the log...

Nowadays customer attrition is increasingly serious in commercial banks. To combat this problem roundly, mining customer evaluation texts is as important as mining customer structured data. In order to extract hidden information from customer evaluations, Textual Feature Selection, Classification and Association Rule Mining are necessary techniques. This paper presents all three techniques by u...

We describe a collection of approaches to inductive querying systems for data that contain segmental structure. The main focus in this chapter is on work done in Helsinki area in 2004-2008. Segmentation is a general data mining technique for summarizing and analyzing sequential data. We first introduce the basic problem setting and notation. We then briefly present an optimal way to accomplish ...

This report examines the problems of customer relationship management (CRM) particularly customer segmentation and customer profiling, and how data mining tools are used to support the decision making. We first describe the steps towards predicting customer’s behavior, such as collecting and preparing data, segmentation and profile modeling. Then, we present a general overview of most used data...

Customer Segmentation is the process of grouping the customers based on their purchase habit. Data mining is useful in finding knowledge from huge amounts of data. The clustering techniques in data mining can be used for the customer segmentation process so that it clusters the customers in such a way that the customers in one group behave similar when compared to the customers in the other gro...

This research work focuses on integrating the multi attribute decision making with data mining in a fuzzy environment for customer relationship management. The main objective is to analyse relation between and considering complex problem of ordering customers segments, which based four criteria customer’s life time value, viz. length (L), recency (R), frequency (F) monetary value (M). proposed ...

Data mining is an important research area in computer science. It is a computational process of determining patterns in large data. Image mining is one of important techniques in data mining, which involved in multiple disciplines. Image Classification Refers the tagging the images into a number of predefined sets. It’s also includes image preprocessing, feature extraction, object detection, ob...

The accurate measurement of cell and nuclei contours are critical for the sensitive and specific detection of changes in normal cells in several medical informatics disciplines. Within microscopy, this task is facilitated using fluorescence cell stains, and segmentation is often the first step in such approaches. Due to the complex nature of cell issues and problems inherent to microscopy, unsu...