Among the post-processing association rule approaches, a promising one is clustering. When an association rule set is clustered, the user is provided with an improved presentation of the mined patterns, since he can have ...

Lazy multi-label learning algorithms have become an important research topic within the multi-label community. These algorithms usually consider the set of standard k-Nearest Neighbors of a new instance to predict its ...

Most existing multi-label classification models focus on distance metrics and feature spare strategies to extract specific features of labels. Those models use the cosine similarity to construct the label correlation matrix ...

There is a widespread belief that clustering is inherently subjective. To quote A. K. Jain, "As a task, clustering is subjective in nature. The same dataset may need to be partitioned differently for different purposes." ...

This paper addresses the edge coloring by total labeling graph problem. This is a labeling of the vertices and edges of a graph such that the weights (colors) of the edges, defined by the sum of its label and the labels ...

The objective of this work is to present the effectiveness and efficiency of algorithms for solving the loss minimization problem in Multi-Label Classification (MLC). We first prove that a specific case of loss minimization ...

A controlled environment based on known properties of the dataset used by a learning algorithm is useful to empirically evaluate machine learning algorithms. Synthetic (artificial) datasets are used for this purpose. ...

A graph G = (V, E) is weighted-k-antimagic if for each w : V -> R, there is an injective function f : E -> {1,...,vertical bar E vertical bar + k} such that the following sums are all distinct: for each vertex u, Sigma(v:uv ...

Feature Selection plays an important role in machine learning and data mining, and it is often applied as a data pre-processing step. This task can speed up learning algorithms and sometimes improve their performance. In ...