资源分类
》 资源 》论文》Clustering High Dimensional Categorical Data via Topographical Features

Clustering High Dimensional Categorical Data via Topographical Features

2020-03-06 | |  101 |   61 |   0

Abstract

Analysis of categorical data is a challenging task. In this paper, we propose to compute topographical features of high-dimensional categorical data. We propose an efficient algorithm to extract modes of the underlying distribution and their attractive basins. These topographical features provide a geometric view of the data and can be applied to visualization and clustering of real world challenging datasets. Experiments show that our principled method outperforms state-of-the-art clustering methods while also admits an embarrassingly parallel property.

上一篇：Estimating the Maximum Expected Value through Gaussian Approximation

下一篇：The Arrow of Time in Multivariate Time Series

用户评价
全部评价

热门资源

  • The Variational S...

    Unlike traditional images which do not offer in...

  • Learning to Predi...

    Much of model-based reinforcement learning invo...

  • Stratified Strate...

    In this paper we introduce Stratified Strategy ...

  • Learning to learn...

    The move from hand-designed features to learned...

  • A Mathematical Mo...

    Direct democracy, where each voter casts one vo...