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|Title: ||A two-step method for clustering mixed categorical and numeric data|
|Authors: ||Shih, Ming-Yi;Jheng, Jar-Wen;Lai, Lien-Fu|
|Keywords: ||Data Mining;Clustering;Mixed Attributes;Co-Occurrence|
|Issue Date: ||2012-07-02T02:03:12Z
|Abstract: ||Various clustering algorithms have been developed to group data into clusters in diverse|
domains. However, these clustering algorithms work effectively either on pure numeric data or on pure
categorical data, most of them perform poorly on mixed categorical and numeric data types. In this
paper, a new two-step clustering method is presented to find clusters on this kind of data. In this
approach the items in categorical attributes are processed to construct the similarity or relationships
among them based on the ideas of co-occurrence; then all categorical attributes can be converted into
numeric attributes based on these constructed relationships. Finally, since all categorical data are
converted into numeric, the existing clustering algorithms can be applied to the dataset without pain.
Nevertheless, the existing clustering algorithms suffer from some disadvantages or weakness, the
proposed two-step method integrates hierarchical and partitioning clustering algorithm with adding
attributes to cluster objects. This method defines the relationships among items, and improves the
weaknesses of applying single clustering algorithm. Experimental evidences show that robust results
can be achieved by applying this method to cluster mixed numeric and categorical data.
|Relation: ||Tamkang Journal of Science and Engineering (TKJSE), 13(1): 11-19|
|Appears in Collections:||[資訊工程學系] 期刊論文|
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