Automatic text classification is the task of organizing documents into pre-determined classes,
generally using machine learning algorithms. Generally speaking, it is one of the most …

The k-means algorithm is a widely used method that starts with an initial partitioning of the
data and then iteratively converges towards the local solution by reducing the Sum of …

This paper presents an Artificial Bee Colony (ABC) optimization based algorithm for co-
clustering of high-dimensional data. The ABC algorithm is used for optimization problems …

Text categorization plays a crucial role in both academic and commercial platforms due to
the growing demand for automatic organization of documents. Kernel-based classification …

This paper presents a new semantic kernel for classification of high-dimensional data in the
framework of Support Vector Machines (SVM). SVMs have gained widespread application …

The stochastic block model is one of the most studied network models for community
detection, and fitting its likelihood function on large-scale networks is known to be …

Feature selection is critical in reducing the size of data and improving classifier accuracy by
selecting an optimum subset of the overall features. Traditionally, each feature is given a …

Co-clustering refers to the simultaneous clustering of objects and their features. It is used as
a clustering technique when the data exhibit similarities only in a subset of features instead …

In this paper we show how the modularity measure can serve as a useful criterion for co-
clustering document-term matrices. We present and investigate the performance of CoClus …

Navigation patterns expressed by sequences of visited web-sites or categories can
characterize the behavior and habits of users. Such web-page routes taken by individuals …