Numerous real-world applications apply categorical data clustering to find hidden patterns in
the data. The K-modes-based algorithm is a popular algorithm for solving common issues in …

Temporal traffic prediction is critical for ITS yet remains challenging in handling complex
spatio-temporal dynamics of traffic systems. The continuous traffic data (eg, traffic flow, and …

Educational Data Mining (EDM) is the application of data mining methods in the educational
domain. In the EDM field, we see mixed data (ie, text and number data types). Grou** or …

In recommendation, both stationary and dynamic user preferences on items are embedded
in the interactions between users and items (eg, rating or clicking) within their contexts …

Multivariate time series (MTS) forecasting is crucial in many real-world applications. To
achieve accurate MTS forecasting, it is essential to simultaneously consider both intra-and …

Heterogeneous attribute data composed of attributes with different types of values are quite
common in a variety of real-world applications. As data annotation is usually expensive …

Clustering is one of the most commonly used techniques for unsupervised data analysis. As
real data sets are usually composed of numerical and categorical features that are …

Data set composed of categorical features is very common in big data analysis tasks. Since
categorical features are usually with a limited number of qualitative possible values, the …

Clustering categorical data is an important task of machine learning, since the type of data
widely exists in real world. However, the lack of an inherent order on the domains of …

Mortality prediction is crucial to evaluate the severity of illness and assist in improving the
prognosis of patients. In clinical settings, one way is to analyze the multivariate time series …