, data aggregation, classification, clustering have been applied for solving uncertainty and imprecision related to sensory …

Clustering of features generated of musical sound recordings proved to be beneficial for further classification tasks …">2011</CitationRef>). We propose to use order constrained solutions in K-means clustering to stabilize the results and improve the … interpretability of the clustering. With this method a further improvement of the misclassification error in the aforementioned …

the multicriteria ordered clustering problem. The model is based on the definition of an inconsistency matrix and only …

data analysis and predictive modeling of information systems. Firstly, they apply clustering to study the information …

-world data as road accidents are uncertain. In this article, the authors are comparing three different clustering techniques …: latent class clustering (LCC), k-modes clustering and BIRCH clustering, on road accident data from an Indian district …

. Authors classify active users more accurately to found clusters. Threshold values are used in both clustering and …

-step hybrid model based on machine learning methods for clustering and classification. First, we assign price states to historical … prices using K-means clustering. These price states are also assigned to the corresponding data of external factors. Second …