Clustering Algorithm for Incomplete Data Sets with Mixed Numeric and Categorical Attributes

Provided by: Science & Engineering Research Support soCiety (SERSC)
Topic: Data Management
Format: PDF
The traditional k-prototypes algorithm is well versed in clustering data with mixed numeric and categorical attributes, while it is limited to complete data. In order to handle incomplete data set with missing values, an improved k-prototypes algorithm is proposed in this paper, which employs a new dissimilarity measure for incomplete data set with mixed numeric and categorical attributes and a new approach to select k objects as the initial prototypes based on the nearest neighbors.

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