We present our perspective of database mining as the con uence of machine learning techniques and the performance emphasis of database technology. We describe three classes of database mining problems involving classi cation, associations, and sequences, and argue that these problems can be uniformly viewed as requiring discovery of rules embedded in massive data. We describe a model and some b...

Despite the efforts made on last decades to center the process of knowledge discovery on the user, the balance between the discovery of unknown and interesting patterns is far from being reached. The discovery of association rules is a paradigmatic case, where this balance is quite difficult to establish. In this paper, we propose a new framework for pattern mining – the Onto4AR. This framework...

This paper describes an algorithm for discovery of convoys in database with proximity log. Traditionally, discovery of convoys covers trajectories databases. This paper presents a model for context-aware browsing application based on the network proximity. Our model uses mobile phone as proximity sensor and proximity data replaces location information. As per our concept, any existing or even e...

Even though goal modeling is an effective approach to requirements engineering, it is known to present a number of difficulties in practice. The paper discusses these difficulties and proposes to couple goal modeling and scenario authoring to overcome them. Whereas existing techniques use scenarios to concretize goals, we use them to discover goals. Our proposal is to define enactable rules whi...

This paper describes a rule discovery system that has been developed as part of an ongoing research project. The system allows discovery of multirelational rules using data from relational databases. The basic assumption of the system is that objects to be analyzed are stored in a set of tables. Multirelational rules discovered would either be used in predicting an unknown object attribute valu...

Abs t rac t . Knowledge discovery in databases, or data mining, is an important objective in the development of dataand knowledge-base systems. An attributeoriented rough set method is developed for knowledge discovery in databases. The method integrates learning from example techniques with rough set theory. An attribute-oriented concept tree ascension technique is first applied in generalizat...

This project [4] centers on regional association rule mining and scoping in spatial datasets. We introduces a methodology for mining spatial association rules and proposes new algorithms to determine the scope of a spatial association rule. We develop a reward-based region discovery framework that employs clustering to find interesting regions. The framework is applied to solve two distinct reg...

Functional genomics is a typical scientific discovery domain characterized by a very large number of attributes (genes) relative to the number of examples (observations). This work presents an approach to subgroup discovery in supervised inductive learning of short rules that are appropriate for human interpretation. The approach is based on the subgroup discovery rule learning framework, enhan...

This paper proposes an approach to behaviour-based discovery of Web Services by which business rules that govern service behaviour are described as a policy. The policy is represented in the form of ontological information and is based on actions relating to the service and conditions for performing them. The standard WS-Policy is used to associate such a policy to the Web Service. With a frame...

In this paper we investigate the feasibility of Knowledge Discovery from Databases (KDD) in order to facilitate the discovery of defeasible rules that represent the ratio decidendi underpinning legal decision making. Moreover we will argue in favour of Defeasible Logic as an appropriate formal system in which the extracted principles should be encoded.