Catálogo de publicaciones - libros

A new strategy for discovering action rules (or interventions) is presented in this paper. The current methods [14], [12], [8] require to discover classification rules before any action rule can be constructed from them. Several definitions of action rules [8], [13], [9], [3] have been proposed. They differ in the generality of their classification parts but they are always constructed from certain pairs of classification rules. Our new strategy defines the classification part of an action rule in a unique way. Also, action rules are constructed from single classification rules. We show how to compute their confidence and support. Action rules are used to reclassify objects. In this paper, we propose a method for measuring the level of reclassification freedom for objects in a decision system.

In this paper, we analyze the influence of the indiscernibility degree, which is a primary parameter in rough clustering, on cluster formation. Rough clustering consists of two steps: (1)assignment of initial equivalence relations and (2)iterative refinement of the initial relations. Indiscernibility degree plays a key role in the second step, but it is not easy to independently analyze its characteristics because it inherits the results of step 1. In this paper, we employ the perfect initial equivalence relations, which were generated according to class labels of data, to seclude the influence of step 1. We first examine the relationship between the threshold value of indiscernibility degree and resultant clusters. After that, we apply random disturbance to the perfect relations, and examine how the result changes. The results demonstrated that the relationships between indiscernibility degree and the number of clusters draw a globally convex but multi-modal curve, and the range of indiscernibility degree that yields best cluster validity may exist on a local minimum around the global one which generates single cluster.

This paper highlights the interest of implicative statistics for classification trees. We start by showing how Gras’ implication index may be defined for the rules derived from an induced decision tree. Then, we show that residuals used in the modeling of contingency tables provide interesting alternatives to Gras’ index. We then consider two main usages of these indexes. The first is purely descriptive and concerns the a posteriori individual evaluation of the classification rules. The second usage, considered for instance by [15], relies upon the intensity of implication to define the conclusion in each leaf of the induced tree.

Graph coloring is used to characterize some properties of graphs. A of a graph (using colors 1,2,...,k) is a coloring of the vertices of such that (i) two neighbors have different colors (proper coloring) and (ii) for each color class there exists a dominating vertex which is adjacent to all other color classes. In this paper, based on a of a graph, we propose a new clustering technique. Additionally, we provide a cluster validation algorithm. This algorithm aims at finding the optimal number of clusters by evaluating the property of . We adopt this clustering technique for discovering a new typology of hospital stays in the French healthcare system.

E-action rules, introduced in [8], represent actionability knowledge hidden in a decision system. They enhance action rules [3] and extended action rules [4], [6], [7] by assuming that data can be either symbolic or nominal. Several efficient strategies for mining e-action rules have been developed [6], [7], [5], and [8]. All of them assume that data are complete. Clearly, this constraint has to be relaxed since information about attribute values for some objects can be missing or represented as multi-values. To solve this problem, we present DEAR_3 which is an e-action rule generating algorithm. It has three major improvements in comparison to DEAR_2: handling data with missing attribute values and uncertain attribute values, and pruning outliers at its earlier stage.

We present our approach to mining and modeling the behavior of database users. In particular, we propose graphic models to capture the database user’s dynamic behavior and focus on applying data mining techniques to the problem of mining and modeling database user behaviors from database trace logs. The experimental results show that our approach can discover and model user behaviors successfully.

The Situation Calculus has been used by Scherl and Levesque to represent beliefs and belief change without modal operators thanks to a predicate plays the role of an accessibility relation. Their approach has been extended by Shapiro et al. to support belief revision. In this extension plausibility levels are assigned to each situation, and the believed propositions are the propositions that are true in all the most plausible accessible situations.

Their solution is quite elegant from a theoretical point of view but the definition of the plausibility assignment, for a given application domain, raises practical problems.

This paper presents a new proposal that does not make use of plausibilities. The idea is to include the knowledge producing actions into the successor state axioms. In this framework each agent may have a different successor state axiom for a given fluent. Then, each agent may have his subjective view of the evolution of the world. Also, agents may know or may not know that a given action has been performed. That is, the actions are not necessarily public.

In the design of the organisation of a multiagent system the concept of role is fundamental. We informally analyse this concept through examples. Then we propose a more formal definition that can be decomposed into: the conditions that have to be satisfied to hold a role, the norms and institutional powers that apply to a role holder. Finally, we present a modal logical framework to represent these concepts.

This paper considers the problem of reasoning on massive amounts of (possibly distributed) data. Presently, existing proposals show some limitations: the quantity of data that can be handled contemporarily is limited, due to the fact that reasoning is generally carried out in main memory; the interaction with external (and independent) DBMSs is not trivial and, in several cases, not allowed at all; the efficiency of present implementations is still not sufficient for their utilization in complex reasoning tasks involving massive amounts of data. This paper provides a contribution in this setting; it presents a new system, called DLV, which aims to solve all these problems.

We present an approach to model supervisory control systems based on extended behaviour networks. In particular, we employ them to formalize the control theory of the supervisor. By separating the reasoning in the supervisor and the action implementation in the controller, the overall system architecture becomes modular, and therefore easily changeable and modifiable.