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Extending the Data Mining Software Packages SAS Enterprise Miner and SPSS Clementine to Handle Fuzzy Cluster Membership: Implementation with Examples

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Identifier	Thesis 1997
Author	Wedding, Donald K.
Title	Extending the Data Mining Software Packages SAS Enterprise Miner and SPSS Clementine to Handle Fuzzy Cluster Membership: Implementation with Examples
Publisher	Central Connecticut State University
Date	2009
Resource Type	Master's Thesis
Notes	Clustering is the process of placing data records into homogenous groups. Members of each group are similar to one another and highly different from members outside the group. The clusters are used for predictive analysis (what will happen), for explaining results (why it will happen), and for profiling and understanding data. Hard clustering and fuzzy clustering are both commonly used by data miners. In hard clustering, membership is absolute. A record is completely in a cluster or it is completely out of a cluster so that membership is mutually exclusive. In fuzzy clustering, a record is permitted to have partial membership so that it is allowed to be in more in one cluster. For example, a record might be 60% in cluster 1 and 40% in cluster 2. There are advantages and disadvantages to both hard and fuzzy clustering, and both techniques are used in research depending upon the data, the type of analysis, and the purpose of the analysis. Although both types of clustering are used in research, only hard clustering is provided by the two most popular commercially available data mining development tools: SAS Enterprise Miner and SPSS Clementine (note that the graduate student preparing this research is an employee of the SAS Institute). Thus, if a researcher needs to use fuzzy clustering, he or she will be unable to use these tools. This research presents a method of approximating fuzzy clusters in both SAS Enterprise Miner and SPSS Clementine by extending their hard clustering tools to incorporate fuzzy membership values. Although these fuzzy memberships of hard clusters are only approximations of fuzzy clusters, it is shown that they can improve accuracy over hard clustering in some situations. Consequently, they should be added to the list of standard techniques employed by data miners.
Subject	Data mining SAS (Computer program language) Fuzzy algorithms
Department	Department of Mathematical Sciences
Advisor	Bilisoly, Roger, 1963-
Type	Text
Digital Format	application/pdf
Language	eng
OCLC number	713734394
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Extending the Data Mining Software Packages SAS Enterprise Miner and SPSS Clementine to Handle Fuzzy Cluster Membership: Implementation with Examples

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