Kernel Discriminant Analysis And Information
Complexity: Advanced Models For Micro-data
Mining And Micro-marketing Solutions
Author(s)
C. Liberati & F. Camillo
Abstract
In this paper we shall consider Kernel Discriminant Analysis as an innovative tool
for supervised classification in a business vision as a marketing solution. The main
idea we propose is the combined use of information complexity and bootstrap
process which allows the user to overcome the open problems of such a technique
as the kernel function choice and at the same time check the robustness of the rule
found.
Keywords: Kernel Discriminant Analysis, information-theoretic complexity
measure, bootstrap process, micro-data mining, marketing solution.
1 From data mining to data base marketing
Today, more than in the past, companies understand the value of collecting
customer data which try to exploit an intelligent system for extracting interesting
information.
The need for a business to get knowledge from data comes from demand to
monitor its own clients in order to preserve its relationship with its customers.
In fact, the scenario with which a business has to face today is really complex:
many customers, many products, many competitors, and little time to react,
it means that customer loyalty is a thing of the past so a company needs to
reinforce the value of its brand providing specific products projected \“around the
customers”. So it is clear how in such situations the use of Data Mining (DM) in
a Knowledge Discovery process (KDD) is dramatically important [1]. The role
of DM therefore consists of helping a company to solve vexing issues and to
address business processes to reach a good impact in the market. Data mining, on
Keywords
Kernel Discriminant Analysis, information-theoretic complexity
measure, bootstrap process, micro-data mining, marketing solution.
Related Book
Other papers in this volume
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