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    <title>Setnes, M.</title>
    <link>http://repub.eur.nl/res/aut/9212/</link>
    <description>List of Publications</description>
    <language>en</language>
    <image>
      <url>http://repub.eur.nl/static-eur/img/logo.png</url>
      <title>RePub, Erasmus University Rotterdam</title>
      <link>http://repub.eur.nl</link>
    </image>
    <item>
      <title>Extended Fuzzy Clustering Algorithms (Research Paper)</title>
      <link>http://repub.eur.nl/res/pub/57/</link>
      <pubDate>2000-11-23T00:00:00Z</pubDate>
      <description>Fuzzy clustering is a widely applied method for obtaining fuzzy models from data. It
has been applied successfully in various fields including finance and marketing. Despite
the successful applications, there are a number of issues that must be dealt with in practical
applications of fuzzy clustering algorithms. This technical report proposes two extensions
to the objective function based fuzzy clustering for dealing with these issues. First, the
(point) prototypes are extended to hypervolumes whose size is determined automatically
from the data being clustered. These prototypes are shown to be less sensitive to a bias
in the distribution of the data. Second, cluster merging by assessing the similarity among
the clusters during optimization is introduced. Starting with an over-estimated number of
clusters in the data, similar clusters are merged during clustering in order to obtain a suitable
partitioning of the data. An adaptive threshold for merging is introduced. The proposed
extensions are applied to Gustafson-Kessel and fuzzy c-means algorithms, and the resulting
extended algorithms are given. The properties of the new algorithms are illustrated in
various examples.</description>
    </item> <item>
      <title>Fuzzy Modeling of Client Preference in Data-Rich Marketing Environments (Research Paper)</title>
      <link>http://repub.eur.nl/res/pub/55/</link>
      <pubDate>2000-11-13T00:00:00Z</pubDate>
      <description>Advances in computational methods have led, in the world of financial services, to huge databases of client and market information. In the past decade, various computational intelligence (CI) techniques have been applied in mining this data for obtaining knowledge and in-depth information about the clients and the markets. This paper discusses the application of fuzzy clustering in target selection from large databases for direct marketing (DM) purposes. Actual data from the campaigns of a large financial services provider are used as a test case. The results obtained with the fuzzy clustering approach are compared with those resulting from the current practice of using statistical tools for target selection.</description>
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