کد مقاله کد نشریه سال انتشار مقاله انگلیسی نسخه تمام متن
410498 679147 2013 8 صفحه PDF دانلود رایگان
عنوان انگلیسی مقاله ISI
A weighting k-modes algorithm for subspace clustering of categorical data
موضوعات مرتبط
مهندسی و علوم پایه مهندسی کامپیوتر هوش مصنوعی
پیش نمایش صفحه اول مقاله
A weighting k-modes algorithm for subspace clustering of categorical data
چکیده انگلیسی

Traditional clustering algorithms consider all of the dimensions of an input data set equally. However, in the high dimensional data, a common property is that data points are highly clustered in subspaces, which means classes of objects are categorized in subspaces rather than the entire space. Subspace clustering is an extension of traditional clustering that seeks to find clusters in different subspaces within a data set. In this paper, a weighting k-modes algorithm is presented for subspace clustering of categorical data and its corresponding time complexity is analyzed as well. In the proposed algorithm, an additional step is added to the k-modes clustering process to automatically compute the weight of all dimensions in each cluster by using complement entropy. Furthermore, the attribute weight can be used to identify the subsets of important dimensions that categorize different clusters. The effectiveness of the proposed algorithm is demonstrated with real data sets and synthetic data sets.

ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Neurocomputing - Volume 108, 2 May 2013, Pages 23–30
نویسندگان
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