— This paper discusses the results of an applied research on the fishing activity based on a monitor system developed by a company and fishing reports produced at the end of each fishing activity. Due to economic interests combined with fishing limitations there is a natural tendency for wrong reporting. We apply Data Mining (DM) methodologies to find fishing patterns. These DM techniques in SQ...

This paper describes a proposal for enhanced visualization of a data-­‐mining model generated with Association Rule (AR) techniques by applying Self-­‐Organizing Maps (SOM). A representation of visual percep-­‐ tion model of AR based on a method called AVM-­‐DM (Augmented Visualiza-­‐ tion Models for Data Mining) is established, together with data and pat-­‐ terns, which support the visual expl...

In many scientific and engineering fields, there are a number of data sets uncontrollable and hard to handle because the nature of measurement of a performance variable may often be destructive or very expensive, which are known as sets of noise factors. Although these noise factors, which may not be controlled by manufacturing and cost reasons, are merged as a key problem of data mining (DM) a...

The aim of my PhD research is focused on Text Mining (TM), one major school in Knowledge Discovery in Data (KDD), and in particular the classification / categorization of documents utilizing novel algorithms for the identification of hidden patterns, rules, regularities and trends within these documents. Two significant techniques of Data Mining (DM), another well-known KDD school, are involved...

The lmitations found in hospital management are directly related to the lack of information and to an inadequate resource management. These aspects are crucial for the management of any organizational entity. This work proposes a Data Mining (DM) approach in order to identify relevant data about patients’ management to provide decision makers with important information to fundament their decisi...

Purpose – Data mining (DM) has been considered to be a tool of business intelligence (BI) for knowledge discovery. Recent discussions in this field state that DM does not contribute to business in a large-scale. The purpose of this paper is to discuss the importance of business insiders in the process of knowledge development to make DM more relevant to business. Design/methodology/approach – T...

firewalls are not appropriate for ad hoc networks), IDS can help to monitor and analyze the traffic in UbiComp applications based on sensor networks. The analysis of audit data for the construction of intrusion detection models can be based on Data Mining algorithms which return frequent activity patterns. However, sensor networks pose new challenging requirements to DM methods, since the solut...

1. INTRODUCTION. Integration of knowledge management (KM) and Data Mining (DM) methods can benefit many applications. It permits to combine and mutually verify knowledge obtained from experts and extracted from raw data. Traditional expert systems rely on knowledge " extracted " in the form of If-Then diagnostic rules from experts. Systems based on Machine Learning technique rely on an availabl...

Proper design of a breeding program has been an issue of primary concern in much animal breeding research during the last decade. Data Mining (DM) is a powerful paradigm for nding patterns that can be used to predict the productivity of progeny given information about their sire, dam and the environment. The more accurate the discovered patterns, the more genetic gain one can achieve in a breed...

The goal of this extended abstract is to contribute to the forum for research on construction of data mining workflows. We briefly introduce a formal framework called FOFRADAR (FOrmal FRAmework for Data mining with Association Rules) and then we outline how it can be used to control a workflow of data mining with association rules. We consider this relevant to associative classifiers that use a...