Data Mining: An Overview from a Database Perspective
IEEE Transactions on Knowledge and Data Engineering
A Survey of Temporal Knowledge Discovery Paradigms and Methods
IEEE Transactions on Knowledge and Data Engineering
Data mining of association structures to model consumer behaviour
Computational Statistics & Data Analysis - Nonlinear methods and data mining
Dimensionality Reduction of Unsupervised Data
ICTAI '97 Proceedings of the 9th International Conference on Tools with Artificial Intelligence
Bounds on the number of hidden neurons in three-layer binary
Neural Networks
An Interactive Approach to Mining Gene Expression Data
IEEE Transactions on Knowledge and Data Engineering
Multicampaign Assignment Problem
IEEE Transactions on Knowledge and Data Engineering
Pattern Discovery of Fuzzy Time Series for Financial Prediction
IEEE Transactions on Knowledge and Data Engineering
Fuzzy c-Means Classifier for Incomplete Data Sets with Outliers and Missing Values
CIMCA '05 Proceedings of the International Conference on Computational Intelligence for Modelling, Control and Automation and International Conference on Intelligent Agents, Web Technologies and Internet Commerce Vol-2 (CIMCA-IAWTIC'06) - Volume 02
Segmenting Customers from Population to Individuals: Does 1-to-1 Keep Your Customers Forever?
IEEE Transactions on Knowledge and Data Engineering
Credit scoring with a data mining approach based on support vector machines
Expert Systems with Applications: An International Journal
Market segmentation based on hierarchical self-organizing map for markets of multimedia on demand
Expert Systems with Applications: An International Journal
Soliciting customer requirements for product redesign based on picture sorts and ART2 neural network
Expert Systems with Applications: An International Journal
Evaluating Variable-Length Markov Chain Models for Analysis of User Web Navigation Sessions
IEEE Transactions on Knowledge and Data Engineering
Clustering over Multiple Evolving Streams by Events and Correlations
IEEE Transactions on Knowledge and Data Engineering
Searching customer patterns of mobile service using clustering and quantitative association rule
Expert Systems with Applications: An International Journal
A Web Usage Mining Framework for Mining Evolving User Profiles in Dynamic Web Sites
IEEE Transactions on Knowledge and Data Engineering
Modeling consumer situational choice of long distance communication with neural networks
Decision Support Systems
Analyzing the Structure and Evolution of Massive Telecom Graphs
IEEE Transactions on Knowledge and Data Engineering
Expert Systems with Applications: An International Journal
A new fuzzy clustering algorithm for optimally finding granular prototypes
International Journal of Approximate Reasoning
Discovering golden nuggets: data mining in financial application
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
Survey of Data Mining Approaches to User Modeling for Adaptive Hypermedia
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
A survey of fuzzy clustering algorithms for pattern recognition. II
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
IEEE Transactions on Fuzzy Systems
A Possibilistic Fuzzy c-Means Clustering Algorithm
IEEE Transactions on Fuzzy Systems
Automatic discovery of rules for predicting network management events
IEEE Journal on Selected Areas in Communications
A class of constrained clustering algorithms for object boundary extraction
IEEE Transactions on Image Processing
Data mining in soft computing framework: a survey
IEEE Transactions on Neural Networks
Customer portfolio analysis using the SOM
International Journal of Business Information Systems
Hi-index | 12.05 |
Data mining (DM) is a new emerging discipline that aims at extracting knowledge from data using several techniques. DM proved to be useful in business where transactional data turned out to be a mine of information about customer purchase habits. Therefore developing customer models (called also profiles in the literature) is an important step for targeted marketing. In this paper, we develop an approach for customer profiling composed of three steps. In the first step, we cluster data with an FCM-based algorithm in order to extract ''natural'' groups of customers. An important feature of our algorithm is that it provides a reliable estimate of the real number of distinct clusters in the data set using the partition entropy as a validity measure. In the second step, we reduce the number of attributes for each computed group of customers by selecting only the ''most important'' ones for that group. We use the information entropy to quantify the importance of an attribute. Consequently, and a result of this second step, we obtain a set of groups each described by a distinct set of attributes (or characteristics). In the third and final step of our model, we build a set of customer profiles each modeled by a backpropagation neural network and trained with the data in the corresponding group of customers. Experimental results on synthetic and large real-world data sets reveal a very satisfactory performance of our approach.