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Title page for etd-0731107-153141


URN etd-0731107-153141 Statistics This thesis had been viewed 2269 times. Download 39 times.
Author Jui-Min Chiu
Author's Email Address No Public.
Department Information Management
Year 2006 Semester 2
Degree Master Type of Document Master's Thesis
Language Chinese&English Page Count 96
Title Relocatable Split-Oriented Fuzzy clustering with Unknown Cluster Number
Keyword
  • Cluster merging
  • Bisecting k-means
  • Cluster analysis
  • Unknown cluster number
  • Hierarchical clustering
  • Fuzzy c-means clustering
  • Fuzzy c-means clustering
  • Hierarchical clustering
  • Unknown cluster number
  • Cluster analysis
  • Bisecting k-means
  • Cluster merging
  • Abstract Cluster analysis is one kind of data mining. Its main feature is “A cluster is a collection of data objects that are similar to one another within the same cluster and dissimilar to the objects in other clusters” [J. Han and M. Kamber, 2001]. Clustering is a useful method to discover the hidden information, so it is applied in many fields, such as DNA analysis, Business Intelligence (BI), Computing Intelligence, etc.
     Fuzzy c-means (FCM) algorithm has been good performance, but it still has some drawbacks. For example, it must get the predefined number of clusters in advance as traditional k-means. However in the real world application, the cluster number usually can’t be known beforehand. Hierarchical clustering doesn’t need to know the number of clusters in advance, but its time complexity is very high. Researchers [M. Steinbach, G. Karypis, V. Kumar, 2000] offer the Bisecting k-means clustering to resolve the problem on cluster number and receive lower time complexity than original hierarchical clustering. But this method still suffers from the fact that once a step is done, it can never be undone which causes a big problem that some data been partitioned into wrong clusters in the previous stage won’t be relocated in following stages.
     Therefore, the purpose of this research is to automatically select the optimal number of clusters and achieve high accuracy. This research integrates the property of bisecting and divisive hierarchical clustering with Fuzzy c-means algorithm, and finally merges the high similar clusters, which maybe wrong partitioned into different clusters, to improve the clustering quality. We present three enhanced relocatable split-oriented fuzzy clustering algorithms called Bisect FCM, Merging Bisect FCM and Split FCM. The experimental results show that our approaches are not only efficient in convergence speed but also effective in clustering quality. They can obtain the correct or near the cluster number and achieve even higher accuracy than the FCM with cluster number known beforehand.
    Advisor Committee
  • Yen-Ju Yang - advisor
  • none - co-chair
  • none - co-chair
  • Files indicate in-campus access only
    Date of Defense 2007-07-02 Date of Submission 2007-07-31


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