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首页> 外文期刊>Neural Networks and Learning Systems, IEEE Transactions on >From Whole to Part: Reference-Based Representation for Clustering Categorical Data
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From Whole to Part: Reference-Based Representation for Clustering Categorical Data

机译：从整体到局部：分类数据聚类的基于参考的表示

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摘要

Dissimilarity measures play a crucial role in clustering and, are directly related to the performance of clustering algorithms. However, effectively measuring the dissimilarity is not easy, especially for categorical data. The main difficulty of the dissimilarity measurement for categorical data is that its representation lacks a clear space structure. Therefore, the space structure-based representation has been proposed to provide the categorical data with a clear linear representation space. This representation improves the clustering performance obviously but only applies to small data sets because its dimensionality increases rapidly with the size of the data set. In this paper, we investigate the possibility of reducing the dimensionality of the space structure-based representation while maintaining the same representation ability. A lightweight representation scheme is proposed by taking a set of representative objects as the reference system (called the reference set) to position other objects in the Euclidean space. Moreover, a preclustering-based strategy is designed to select an appropriate reference set quickly. Finally, the representation scheme together with the k-means algorithm provides an efficient method to cluster the categorical data. The theoretical and the experimental analysis shows that the proposed method outperforms state-of-the-art methods in terms of both accuracy and efficiency.
机译：差异性度量在聚类中起着至关重要的作用，并且与聚类算法的性能直接相关。但是，有效地测量差异并不容易，尤其是对于分类数据而言。分类数据的相异性度量的主要困难是其表示缺乏清晰的空间结构。因此，已经提出了基于空间结构的表示，以为分类数据提供清晰的线性表示空间。此表示明显改善了聚类性能，但仅适用于小型数据集，因为其维数随数据集的大小而迅速增加。在本文中，我们研究了在保持相同表示能力的同时降低基于空间结构的表示的维数的可能性。通过将一组代表性对象作为参考系统（称为参考集）来将其他对象放置在欧几里得空间中，提出了一种轻量表示方案。此外，基于预聚类的策略旨在快速选择合适的参考集。最后，表示方案与k-means算法一起提供了一种有效的方法来对分类数据进行聚类。理论和实验分析表明，该方法在准确性和效率上均优于最新方法。

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