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- W2734734306 abstract "In recent years, combining multiple attributes of data sets including numerical and categorical attributes for data stream clustering has been a popular practice for improving clustering accuracy. In this paper, we propose a novel data stream clustering algorithm called WKStream which can cluster numerical and categorical data stream with different shapes efficiently. As different attributes have different significances in clustering process, different weights should be assigned for them. Accordingly, we design a new concept called Nonuniformity to assign weight to numerical attributes; While for the categorical attribute, we design a novel method to measure the distance between two values in the same categorical attribute. To discover clusters of different shapes, the concepts of micro-cluster and macro-cluster are utilized and extended to model complex cluster structures. Experimental results conducted on numerical, categorical and mixture real-world data sets have validated the effectiveness of the proposed method." @default.
- W2734734306 created "2017-07-21" @default.
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- W2734734306 date "2017-05-01" @default.
- W2734734306 modified "2023-09-22" @default.
- W2734734306 title "Weighted numerical and categorical attribute clustering in data streams" @default.
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- W2734734306 doi "https://doi.org/10.1109/ijcnn.2017.7966237" @default.
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