Constraining the Search Space in Temporal Pattern Mining

Agents in dynamic environments have to deal with complex situations including various temporal interrelations of actions and events. Discovering frequent patterns in such scenes can be useful in order to create prediction rules which can be used to predict future activities or situations. We present the algorithm MiTemP which learns frequent patterns based on a time intervalbased relational representation. Additionally the problem has also been transfered to a pure relational association rule mining task which can be handled by WARMR. The two approaches are compared in a number of experiments. The experiments show the advantage of avoiding the creation of impossible or redundant patterns with MiTemP. While less patterns have to be explored on average with MiTemP more frequent patterns are found at an earlier refinement level

Towards a Coherent Theory of Physics and Mathematics

As an approach to a Theory of Everything a framework for developing a coherent theory of mathematics and physics together is described. The main characteristic of such a theory is discussed: the theory must be valid and and sufficiently strong, and it must maximally describe its own validity and sufficient strength. The mathematical logical definition of validity is used, and sufficient strength is seen to be a necessary and useful concept. The requirement of maximal description of its own validity and sufficient strength may be useful to reject candidate coherent theories for which the description is less than maximal. Other aspects of a coherent theory discussed include universal applicability, the relation to the anthropic principle, and possible uniqueness. It is suggested that the basic properties of the physical and mathematical universes are entwined with and emerge with a coherent theory. Support for this includes the indirect reality status of properties of very small or very large far away systems compared to moderate sized nearby systems. Discussion of the necessary physical nature of language includes physical models of language and a proof that the meaning content of expressions of any axiomatizable theory seems to be independent of the algorithmic complexity of the theory. G\"{o}del maps seem to be less useful for a coherent theory than for purely mathematical theories because all symbols and words of any language musthave representations as states of physical systems already in the domain of a coherent theory.Comment: 38 pages, earlier version extensively revised and clarified. Accepted for publication in Foundations of Physic

From Classification Rules to Action Recommendations

Rule induction has attracted a great deal of attention in Machine Learning and Data Mining. However, generating rules is not an end in itself because their applicability is not straightforward especially when the number of rules is large. Ideally, the user would ultimately like to use these rules to decide which actions to take. In the literature, this notion is usually referred to as actionability. The contribution of this paper1 is two-fold: first we propose a survey of the main approaches developed to address actionability. This topic has received growing attention in the past years. We present a classification of the main research in this area as well as a comparative study between the different approaches. Second, we propose a new framework to address actionability. Our goal is to lighten the burden of analyzing a large set of classification rules when the user is confronted with an "unsatisfactory situation" and needs help to decide what appropriate actions to take in order to remedy the situation. The method consists in comparing the situation to a set of classification rules. This is achieved by using a suitable distance that allows one to suggest action recommendations requiring minimal changes to improve the situation. We propose the algorithm DAKAR for learning action recommendations and we present an application to environment protection. Our experiment shows the usefulness of our contribution for action recommendation but also raises some concerns about the impact of the redundancy of a set of rules in learning action recommendations of good quality

Discovering Unexpected Patterns in Temporal Data Using Temporal Logic

There has been much attention given recently to the task of finding interesting patterns in temporal databases. Since there are so many different approaches to the problem of discovering temporal patterns, we first present a characterization of different discovery tasks and then focus on one task of discovering interesting patterns of events in temporal sequences. Given an (infinite) temporal database or a sequence of events one can, in general, discover an infinite number of temporal patterns in this data. Therefore, it is important to specify some measure of interestingness for discovered patterns and then select only the patterns interesting according to this measure. We present a probabilistic measure of interestingness based on unexpectedness, whereby a pattern P is deemed interesting if the ratio of the actual number of occurrences of P exceeds the expected number of occurrences of P by some user defined threshold. We then make use of a subset of the propositional, linear temporal logic and present an efficient algorithm that discovers unexpected patterns in temporal data. Finally, we apply this algorithm to synthetic data, UNIX operating system calls, and Web logfiles and present the results of these experiments.Information Systems Working Papers Serie