A two dimensional data matrix has been widely used in many applications. The lossless compression of data matrix not only brings benefits for storage but also for network transmission. In this paper, ...

This paper considers the clustering problem for large data sets. We propose an approach based on distributed optimization. The clustering problem is formulated as an optimization problem of maximizing ...

Data Compression and Summarization: • Clustering can simplify large and complex dataset 3. Data Preprocessing for Machine Learning: • Before applying machine learning algorithms, clustering ...

However, this leads to increasing demand for disk storage, as the sizes of the databases containing such data can easily reach dozens of terabytes. In his article "Context binning, model clustering ...

However, its applications extend beyond merely segmenting data; one fascinating use case is image compression. In this tutorial, we'll delve into the intricacies of K-means clustering, starting from ...

Clustering is a grouping of different data points which are similar to each other and form different groups which contain similar data points. For example, if we are having a dataset that contains the ...

We demonstrated the value of utilizing rich time-series data and underscored the importance of careful selection of sampling times for a given experimental system. The results also indicated that ...