This paper discusses the relevance and possible applications of evolutionary algorithms, particularly genetic algorithms, in the domain of knowledge discovery in databases. Knowledge discovery in databases is a process of discovering knowledge along with its validity, novelty, and potentiality. Various genetic-based feature selection algorithms with their pros and cons are discussed in this art...

There are many methods for finding association rules in very large data. However it is well known that most general association rule discovery methods find too many rules, which include a lot of uninteresting rules. Furthermore, the performances of many such algorithms deteriorate when the minimum support is low. They fail to find many interesting rules even when support is low, particularly in...

In the Wason (1960) rule discovery task reasoners must infer a rule that governs the production of number sequences. The task instructions are designed such that reasoners’ initial hypotheses are invariantly narrower than the correct rule. The inferential challenge lies in discovering the scope of these initial hypotheses. The traditional task departs from realworld hypothesis testing in at lea...

In [9], we presented a modified version of Ant-Miner (i.e. Ant-Miner2), where the core computation heuristic value was based on a simple density estimation heuristic. In this paper, we present a further study and introduce another ant-based algorithm, which uses a different pheromone updating strategy and state transition rule. By comparison with the work of Parpinelli et al, our method can imp...

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Sentiment analysis refers to the task of extracting the sentiment of people from textual data. This analysis can be employed to reflect the public’s ideas about an issue stated in natural language text. On the other hand, causal rules are associations between different concepts in a context that one (or several concepts) cause(s) the other(s). In this paper, we propose a new concept ”sentimenta...

The introduction of Lipinski's 'Rule of Five' has initiated a profound shift in the thinking paradigm of medicinal chemists. Understanding the difference between biologically active small molecules and drugs became a priority in the drug discovery process, and the importance of addressing pharmacokinetic properties early during lead optimization is a clear result. These concepts of 'drug-likene...

Spatial data mining, i.e., discovery of interesting, implicit knowledge in spatial databases, is an important task for understanding and use of spatial data-and knowledge-bases. Statistical analysis has been the main method used for analyzing spatial data. Unfortunately, it has a number of weeknesses. In this paper, a number of methods based on knowledge discovery techniques for large databases...