In many financial applications, such as fraud detection, reject inference, and credit
evaluation, detecting clusters automatically is critical because it helps to understand the …

Different aggregation levels of the electric grid's big data can be helpful to develop highly
accurate deep learning models for Short-term Load Forecasting (STLF) in electrical …

Kera**an merupakan salah satu bagian dari 14 lini industri kreatif yang cukup potensial
mendorong kemajuan perekonomian Indonesia. Potensialnya, lini industri kera**an …

The importance of unsupervised clustering methods is well established in the statistics and
machine learning literature. Many sophisticated unsupervised classification techniques have …

The k-means algorithm, known for its simplicity and adaptability, faces challenges related to
manual cluster number selection and sensitivity to initial centroid placement. This paper …

Learning from previous events represents a crucial element to improve the design and
operations of industrial processes, especially considering the many variables characterizing …

Benchmark datasets with predefined cluster structures and high-dimensional biomedical
datasets outline the challenges of cluster analysis: clustering algorithms are limited in their …

K-means is one of the most algorithms used in unsupervised machine learning. Numerous
metrics are adapted to k-means for estimating the optimal number of clusters k-optimal. In …

Background Manic and depressive mood states in bipolar disorder (BD) may emerge from
the non‐linear relations between constantly changing mood symptoms exhibited as a …

Massive electrical load exhibits many patterns making it difficult for forecast algorithms to
generalise well. Most learning algorithms produce a better forecast for dominant patterns in …