& This paper presents a subgroup discovery algorithm APRIORI-SD, developed by adapting association rule learning to subgroup discovery. The paper contributes to subgroup discovery, to a better understanding of the weighted covering algorithm, and the properties of the weighted relative accuracy heuristic by analyzing their performance in the ROC space. An experimental comparison with rule learn...

This work is dedicated to one of the most urgent problems in the field of process mining. Process mining is a technique that offers plenty of methods for the discovery and analysis of business processes based on event logs. However, there is a lack of real process models and event logs, which can be used to verify the methods developed to achieve process mining goals. Hence, there is a need in ...

In this paper, we present the discovery of rules for different challenges encountered in video indexing. These rules should be considered as knowledge that can be used as a guideline for the development of better indexing tools. We use a fuzzy decision tree to extract the rules based on color proportions of key-frames extracted from one single video-news. Experimental results and comparisons wi...

Feature extraction and knowledge discovery from a large amount of image data such as remote sensing images have become highly required recent years. In this study, we present a framework for data mining from a set of time-series images including moving objects using clustering by self-organizing mapping(SOM) and extraction of time-dependent association rules. We applied this method to weather s...

Subgroup discovery aims at constructing symbolic rules that describe statistically interesting subsets of instances with a chosen property of interest. Semantic subgroup discovery extends standard subgroup discovery approaches by exploiting ontological concepts in rule construction. Compared to previously developed semantic data mining systems SDM-SEGS and SDM-Aleph, this paper presents a gener...

Numerous measures are used for performance evaluation in machine learning. In predictive knowledge discovery, the most frequently used measure is classification accuracy. With new tasks being addressed in knowledge discovery, new measures appear. In descriptive knowledge discovery, where induced rules are not primarily intended for classification, new measures used are novelty in clausal and su...

Although there is extensive literature in software defect prediction techniques, machine learning approaches have yet to be fully explored and in particular, Subgroup Discovery (SD) techniques. SD algorithms aim to find subgroups of data that are statistically different given a property of interest [1,2]. SD lies between predictive (finding rules given historical data and a property of interest...

Learning query transformation rules is vital for the success of semantic query optimization in domains where the user cannot provide a comprehensive set of integrity constraints. Finding these rules is a discovery task because of the lack of target. Previous approaches to learning query transformation rules have been based on analyzing past queries. We propose a new approach to learning query t...

This work describes an algorithm for rule discovery in databases called AntMiner. The objective of the algorithm is the extraction of classification rules to be applied to unseen data as a decision aid. The algorithm used to discover such rules is inspired in the behavior of a real ant colony, as well as some concepts of information theory and data mining. AntMiner was applied to medical databa...

Structured attributes have domains (value sets) that are partially ordered sets, typically hierarchies. Such attributes allow knowledge discovery programs to incorporate background knowledge about hierarchical relationships among attribute values. Inductive generalization rules for structured attributes have been developed that take into consideration the type of nodes in the domain hierarchy (...