NECA: Network-Embedded Deep Representation Learning for Categorical Data

TL;DR

NECA is a novel deep learning method that embeds categorical data into numeric vectors by capturing intrinsic relationships, enhancing tasks like clustering with demonstrated effectiveness.

Contribution

NECA introduces a network-embedded deep representation learning approach tailored for categorical data, improving data embedding quality for downstream tasks.

Findings

Effective clustering performance demonstrated

Captures intrinsic relationships among attribute values

Outperforms existing embedding methods

Abstract

We propose NECA, a deep representation learning method for categorical data. Built upon the foundations of network embedding and deep unsupervised representation learning, NECA deeply embeds the intrinsic relationship among attribute values and explicitly expresses data objects with numeric vector representations. Designed specifically for categorical data, NECA can support important downstream data mining tasks, such as clustering. Extensive experimental analysis demonstrated the effectiveness of NECA.