Clustering data objects into homogeneous groups is one of the most important
tasks in data mining. Spectral clustering is arguably one of the most important
algorithms for clustering, as it is appealing for its theoretical soundness and
is adaptable to many real-world data settings. For example, mixed data, where
the data is composed of numerical and categorical features, is typically
handled via numerical discretization, dummy coding, or similarity computation
that takes into account both data types. This paper explores a more natural way
to incorporate both numerical and categorical information into the spectral
clustering algorithm, avoiding the need for data preprocessing or the use of
sophisticated similarity functions. We propose adding extra nodes corresponding
to the different categories the data may belong to and show that it leads to an
interpretable clustering objective function. Furthermore, we demonstrate that
this simple framework leads to a linear-time spectral clustering algorithm for
categorical-only data. Finally, we compare the performance of our algorithms
against other related methods and show that it provides a competitive
alternative to them in terms of performance and runtime.

通过将数值和类别信息结合到谱聚类算法中，本文提出一种新的方法来处理混合数据，避免了数据预处理或使用复杂的相似性函数，并展示了相应的聚类目标函数和线性时间复杂度的算法。与其他相关方法相比，实验证明该算法在性能和运行时间上具有竞争力。

通过额外图节点对分类和混合类型数据进行谱聚类

Spectral Clustering of Categorical and Mixed-type Data via Extra Graph  Nodes

We have described a novel approach for training tabular data using the
TabTransformer model with self-supervised learning. Traditional machine
learning models for tabular data, such as GBDT are being widely used though our
paper examines the effectiveness of the TabTransformer which is a Transformer
based model optimised specifically for tabular data. The TabTransformer
captures intricate relationships and dependencies among features in tabular
data by leveraging the self-attention mechanism of Transformers. We have used a
self-supervised learning approach in this study, where the TabTransformer
learns from unlabelled data by creating surrogate supervised tasks, eliminating
the need for the labelled data. The aim is to find the most effective
TabTransformer model representation of categorical and numerical features. To
address the challenges faced during the construction of various input settings
into the Transformers. Furthermore, a comparative analysis is also been
conducted to examine performance of the TabTransformer model against baseline
models such as MLP and supervised TabTransformer.
The research has presented with a novel approach by creating various variants
of TabTransformer model namely, Binned-TT, Vanilla-MLP-TT, MLP- based-TT which
has helped to increase the effective capturing of the underlying relationship
between various features of the tabular dataset by constructing optimal inputs.
And further we have employed a self-supervised learning approach in the form of
a masking-based unsupervised setting for tabular data. The findings shed light
on the best way to represent categorical and numerical features, emphasizing
the TabTransormer performance when compared to established machine learning
models and other self-supervised learning methods.

使用自监督学习的 TabTransformer 模型，通过描述一种新颖的表格数据训练方法，提高了对分类和数值特征的建模能力。通过对比传统机器学习模型和自监督学习方法，研究结果显示 TabTransformer 在表格数据上的性能优势。