Abstract. This paper describes FOIL, a system that learns Horn clauses from data expressed as relations. FOIL is based on ideas that have proved effective in attribute-value learning systems, but extends them to a first-order formalism. This new system has been applied successfully to several tasks taken from the machine learning literature.

hill climbing Abstract. Conceptual clustering is an important way of summarizing and explaining data. However, the recent formulation of this paradigm has allowed little exploration of conceptual clustering as a means of improving performance. Furthermore, previous work in conceptual clustering has not explicitly dealt with constraints imposed by real world environments. This article presents COBWEB, a conceptual clustering system that organizes data so as to maximize inference ability. Additionally, COBWEB is incremental and computationally economical, and thus can be flexibly applied in a variety of domains. 1.

. This paper deals with learning first-order logic rules from data lacking an explicit classification predicate. Consequently, the learned rules are not restricted to predicate definitions as in supervised inductive logic programming. First-order logic offers the ability to deal with structured, multi-relational knowledge. Possible applications include first-order knowledge discovery, induction of integrity constraints in databases, multiple predicate learning, and learning mixed theories of predicate definitions and integrity constraints. One of the contributions of our work is a heuristic measure of confirmation, trading off novelty and satisfaction of the rule. The approach has been implemented in the Tertius system. The system performs an optimal bestfirst search, finding the k most confirmed hypotheses, and includes a non-redundant refinement operator to avoid duplicates in the search. Tertius can be adapted to many different domains by tuning its parameters, and it can deal eithe...

This paper traces the development of the main ideas that have led to the present state of knowledge in Inductive Logic Programming. The story begins with research in psychology on the subject of human concept learning. Results from this research influenced early efforts in Artificial Intelligence which combined with the formal methods of inductive inference to evolve into the present discipline of Inductive Logic Programming.

A drawback of traditional data mining methods is that they do not leverage prior knowledge of users. In many business settings, managers and analysts have significant intuition based on several years of experience. In prior work we proposed a method that could discover unexpected patterns in data by using this domain knowledge in a systematic manner. In this paper we continue our focus on discovering unexpected patterns and propose new methods for discovering a minimal set of unexpected patterns that discover orders of magnitude fewer patterns and yet retain most of the truly unexpected ones. We demonstrate the strengths of this approach experimentally using a case study application in a marketing domain.

Abstract. Relational reinforcement learning is the application of reinforcement learning to structured state descriptions. Model-based methods learn a policy based on a known model that comprises a description of the actions and their effects as well as the reward function. If the model is initially unknown, one might learn the model first and then apply the model-based method (indirect reinforcement learning). In this paper, we propose a method for model-learning that is based on a combination of several SVMs using graph kernels. Indeterministic processes can be dealt with by combining the kernel approach with a clustering technique. We demonstrate the validity of the approach by a range of experiments on various Blocksworld scenarios. 1

Knowledge Management involves acquisition, enhancement and utilization of organizational knowledge. Given the enormous quantities of data stored in organizational data warehouses, it stands to reason that data mining approaches could contribute significantly to the Knowledge Management process at hand. However most current data mining methods are primarily data-driven and do not systematically leverage prior organizational knowledge, a substantial amount of which exists. In this paper we provide a new approach based on knowledge-driven data mining techniques that systematically leverages and enhances organizational knowledge. This iterative approach starts with prior domain knowledge, tacit or explicit, and involves three sequential automated data mining modules: (i) The Advocate, which generates patterns consistent with prior knowledge. (ii) The Devil’s Advocate, which then challenges current knowledge and generates patterns that contradict knowledge and (iii) The Jury, which reconciles patterns generated by the prior two modules and builds refined knowledge. Each of these modules in theory can have several implementations based on factors such as (a) how “knowledge ” is formally represented, (b) how the strength of patterns is represented and (c) what the intelligence and heuristics used in the discovery engines are. In this paper we also describe in detail data mining methods that constitute one specific implementation of these modules. The effectiveness of this approach is demonstrated using a comprehensive real-world case study application in the consumer purchase domain. 1.