In evaluating the results of cluster analysis, it is common practice to make use of a number of
fixed heuristics rather than to compare a data clustering directly against an empirically …

Supervised feature selection research has a long history. Its popularity exploded in the past
30 years due to the advance of information technology and the need to analyze high …

In the context of machine learning from examples this paper deals with the problem of
estimating the quality of attributes with and without dependencies among them. Kira and …

Relief algorithms are general and successful attribute estimators. They are able to detect
conditional dependencies between attributes and provide a unified view on the attribute …

Current inductive machine learning algorithms typically use greedy search with limited
lookahead. This prevents them to detect significant conditional dependencies between the …

Heuristic measures for estimating the quality of attributes mostly assume the independence
of attributes so in domains with strong dependencies between attributes their performance is …

Data mining is often referred to by real-time users and software solutions providers as
knowledge discovery in databases (KDD). Good data mining practice for business …

We analyse the biases of eleven measures for estimating the quality of the multi-valued
attributes. The values of information gain, J-measure, gini-index, and relevance tend to …

Feature selection is an important preprocessing technology for dimensionality reduction,
which reduces the dimension of the dataset by acquiring a subset of features with the largest …

Similarity and dissimilarity measures are widely used in many research areas and
applications. When a dissimilarity measure is used, it is normally required to be a distance …