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- W2896987316 abstract "Similarity of nominal data plays fundamental roles in numerous fields of both machine learning and data mining. Unlike the similarity of numerical data, that of nominal data is much more difficult to describe, and few efforts have been done for it. Although existing nominal similarity measures can reveal a part of data properties, they suffer from low accuracy due to ignoring value relationships or integrating multi-view relationships inappropriately. In this paper, we propose a novel hierarchical measure for nominal data similarity (HNS). The HNS leverages the intrinsic data characteristics by considering low-level information both within and between attributes, and hierarchically seizes the value distributions, attribute interactions and attribute to object contributions. Meanwhile, it aggregates multi-view relationships trough a bottom to top framework, remaining consistency as well as complementary details. We theoretically analyzed this measure, and experiments on six UCI data sets demonstrate that the HNS outperforms the state-of-the-art nominal similarity measures in term of target alignment and clustering accuracy." @default.
- W2896987316 created "2018-10-26" @default.
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- W2896987316 date "2018-07-01" @default.
- W2896987316 modified "2023-10-16" @default.
- W2896987316 title "Nominal Data Similarity: A Hierarchical Measure" @default.
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- W2896987316 doi "https://doi.org/10.1109/ijcnn.2018.8488994" @default.
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