Nada Lavrač

Relational Data Mining

We may not be able to make you love reading, but relational data mining will lead you to love reading starting from now. Book is the window to open the new world. The world that you want is in the better stage and level. World will always guide you to even the prestige stage of the life. You know, this is some of how reading will give you the kindness. In this case, more books you read more knowledge you know, but it can mean also the bore is full.

Rule Evaluation Measures: A Unifying View

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 subgroup discovery, and support and confidence in association rule learning. Additional measures are needed as many descriptive knowledge discovery tasks involve the induction of a large set of redundant rules and the problem is the ranking and filtering of the induced rule set. In this paper we develop a unifying view on some of the existing measures for predictive and descriptive induction. We provide a common terminology and notation by means of contingency tables. We demonstrate how to trade off these measures, by using what we call weighted relative accuracy. The paper furthermore demonstrates that many rule evaluation measures developed for predictive knowledge discovery can be adapted to descriptive knowledge discovery tasks.

Selected techniques for data mining in medicine.

Widespread use of medical information systems and explosive growth of medical databases require traditional manual data analysis to be coupled with methods for efficient computer-assisted analysis. This paper presents selected data mining techniques that can be applied in medicine, and in particular some machine learning techniques including the mechanisms that make them better suited for the analysis of medical databases (derivation of symbolic rules, use of background knowledge, sensitivity and specificity of induced descriptions). The importance of the interpretability of results of data analysis is discussed and illustrated on selected medical applications.

Propositionalization approaches to relational data mining

This chapter surveys methods that transform a relational representation of a learning problem into a propositional (feature-based, attribute-value) representation. This kind of representation change is known as propositionalization. Taking such an approach, feature construction can be decoupled from model construction. It has been shown that in many relational data mining applications this can be done without loss of predictive performance. After reviewing both general-purpose and domain-dependent propositionalization approaches from the literature, an extension to the LINUS propositionalization method that overcomes the systems earlier inability to deal with non-determinate local variables is described.

Expert-guided subgroup discovery: methodology and application

This paper presents an approach to expert-guided subgroup discovery. The main step of the subgroup discovery process, the induction of subgroup descriptions, is performed by a heuristic beam search algorithm, using a novel parametrized definition of rule quality which is analyzed in detail. The other important steps of the proposed subgroup discovery process are the detection of statistically significant properties of selected subgroups and subgroup visualization: statistically significant properties are used to enrich the descriptions of induced subgroups, while the visualization shows subgroup properties in the form of distributions of the numbers of examples in the subgroups. The approach is illustrated by the results obtained for a medical problem of early detection of patient risk groups.

Knowledge Discovery in Databases: PKDD 2003

This paper describes the Robosail project. It started in 1997 with the aim to build a self-learning auto pilot for a single handed sailing yacht. The goal was to make an adaptive system that would help a single handed sailor to go faster on average in a race. Presently, after five years of development and a number of sea trials, we have a commercial system available (www.robosail.com). It is a hybrid system using agent technology, machine learning, data mining and rule-based reasoning. Apart from describing the system we try to generalize our findings, and argue that sailing is an interesting paradigm for a class of hybrid systems that one could call Skill-based Systems.

APRIORI-SD: ADAPTING ASSOCIATION RULE LEARNING TO SUBGROUP DISCOVERY

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 learners CN2, RIPPER, and APRIORI-C on UCI data sets demonstrates that APRIORI-SD produces substantially smaller rulesets, where individual rules have higher coverage and significance. APRIORI-SD is also compared to subgroup discovery algorithms CN2-SD and SubgroupMiner. The comparisons performed on U.K. traffic accident data show that APRIORI-SD is a competitive subgroup discovery algorithm.

Learning Nonrecursive Definitions of Relations with LINUS

Many successful inductive learning systems use a propositional attribute-value language to represent both training examples and induced hypotheses. Recent developments are concerned with systems that induce concept descriptions in first-order logic. The deductive hierarchical database (DHDB) formalism is a restricted form of Horn clause logic in which nonrecursive logical definitions of relations can be expressed. Having variables, compound terms and predicates, the DHDB formalism allows for more compact descriptions of concepts than an attribute-value language. Our inductive learning system LINUS uses the DHDB formalism to represent concepts as definitions of relations. The paper gives a description of LINUS and presents the results of its successful application to several inductive learning tasks taken from the machine learning literature. A comparison with the results of other first-order learning systems is given as well.

Intelligent Data Analysis in Medicine and Pharmacology

From the Publisher: Intelligent Data Analysis in Medicine and Pharmacology consists of selected (and thoroughly revised) papers presented at the First International Workshop on Intelligent Data Analysis in Medicine and Pharmacology (IDAMAP-96) held in Budapest in August 1996 as part of the 12th European Conference on Artificial Intelligence (ECAI-96). IDAMAP-96 was organized with the motivation to gather scientists and practitioners interested in computational data analysis methods applied to medicine and pharmacology, aimed at narrowing the increasing gap between excessive amounts of data stored in medical and pharmacological databases on the one hand, and the interpretation, understanding and effective use of stored data on the other hand. Besides the revised Workshop papers, the book contains a selection of contributions by invited authors. The expected readership of Intelligent Data Analysis in Medicine and Pharmacology is researchers and practitioners interested in intelligent data analysis, data mining, and knowledge discovery in databases, particularly those who are interested in using these technologies in medicine and pharmacology. Researchers and students in artificial intelligence and statistics should find this book of interest as well. Finally, much of the presented material will be interesting to physicians and pharmacologists challenged by new computational technologies, or simply in need of effectively utilizing the overwhelming volumes of data collected as a result of improved computer support in their daily professional practice.

Foundations of Rule Learning

Rules the clearest, most explored and best understood form of knowledge representation are particularly important for data mining, as they offer the best tradeoff between human and machine understandability. This book presents the fundamentals of rule learning as investigated in classical machine learning and modern data mining. It introduces a feature-based view, as a unifying framework for propositional and relational rule learning, thus bridging the gap between attribute-value learning and inductive logic programming, and providing complete coverage of most important elements of rule learning. The book can be used as a textbook for teaching machine learning, as well as a comprehensive reference to research in the field of inductive rule learning. As such, it targets students, researchers and developers of rule learning algorithms, presenting the fundamental rule learning concepts in sufficient breadth and depth to enable the reader to understand, develop and apply rule learning techniques to real-world data.