Itemsets provide local descriptions of the data.This work proposes to use itemsets as basic means for classification purposes too.To enable this, the concept of class support SUpi of an itemset is introduced, i.e., how many times an itemset occurs when a specific class Ci is present.Class supports of frequent itemsets are computed in the training phase.Upon arrival of a new case to be classified, some of the generated itemsets are selected and their class supports SUpi are used to compute the probability that the case belongs to class cc The result is the class ci with highest such probability.We show that selecting and combining many and long itemsets providing new evidence ( interesting) is an effective strategy for computing the class probabilities.The proposed classification technique is called Large Bayes as it happens to reduce to Naive Bayes classifier when all itemsets selected are of size one only.Experimental results on a large number of benchmark data sets show that Large Bayes consistently outperforms the widely used Ndive Bayes classifier.In many cases, Large Bayes is also superior to other state of the art classification methods such as C4.5, CBA (a recently proposed rule classifier built from association rules), and TAN (a Bayesian network extension of Ndive Bayes).

Our aim is to predict stock markets using information contained in articles published on the Web, mostly textual articles appearing in the leading and influential financial newspapers. From those articles the daily closing values of major stock market indices in Asia, Europe and America are predicted. Textual statements contain not only the effect but also why it happened. A prediction system has been built that uses data mining techniques and sophisticated keyword tuple counting and transformation to produce periodically forecasts in stock markets. Exploiting textual information in addition to numeric time series data increases the quality of the input, hence improved predictions are expected. The forecasts are available in real-time via the Internet Web site. The system's accuracy for this difficult but also extremely challenging application is highly promising.

We define a new fixpoint semantics for rule based reasoning in the presence of weighted information. The semantics is illustrated on a real world application requiring such reasoning. Optimizations and approximations of the semantics are shown so as to make the semantics amenable to very large scale real world applications. We finally prove that the semantics is probabilistic and reduces to the usual fixpoint semantics of stratified Datalog if all information is certain. We implemented various knowledge discovery systems which automatically generate such probabilistic decision rules. In collaboration with a bank in Hong Kong we use one such system to forecast currency exchange rates.>

Two main topics are addressed. First, an algebraic approach is presented to define a general notion of expressive power. Heterogeneous algebras represent information systems and morphisms represent the correspondences between the instances of databases, the correspondences between answers, and the correspondences between queries. An important feature of this new notion of expressive power is that query languages of different types can be compared with respect to their expressive power. In the case of relational query languages, the new notion of expressive power is shown to be equivalent to the notion used by Chandra and Harel. In the case of nonrelational query languages, the versatility of the new notion of expressive power is demonstrated by comparing the fixpoint query languages with an object-oriented query language called FQL. The expressive power of the Functional Query Language FQL is the second main topic of this paper. The specifications of FQL functions can be recursive or even mutually recursive, FQL has a fixpoint semantics based on a complete lattice consisting of bag functions. The query language FQL is shown to be more expressive than the fixpoint query languages. This result implies that FQL is also more expressive than Datalog with stratified negation. Examples of recursive FQL functions are given that determine the ancestors of persons and the bill of materials.

A technique to compute a solution for a given update and a given knowledge base is presented. The salient features of this approach are: the problem is tackled in a general way and presents a technique for repairing inconsistency; the user can interact with the system, actively influence the solution to be generated, and is not forced to generate all possible minimal solutions from which the user finally draws the most preferred one; solutions are obtained by set-oriented fact processing rather than by single fact accesses to the old knowledge base; and, in contrast to the other proposed techniques, the consistency of the old knowledge base is exploited when generating a solution.>