One of the central problems in the eld of knowledge discovery is the development ofgood measures of interestingness of discovered patterns. Such measures of interestingness are divided into objective measures -- those that depend only on the structure of a pattern and the underlying data used in the discovery process, and the subjective measures -- those that also depend on the class of users who examine the pattern. The focus of this paper is on studying subjective measures of interestingness. These measures are classified into actionable and unexpected, and the relationship between them is examined. The unexpected measure of interestingness is defined in terms of the belief system that the user has. Interestingness of a pattern is expressed in terms of how it affects the belief system. The paper also discusses how this unexpected measure of interestingness can be used in the discovery process.

The paper presents a multidimensional (MD) approach to recommender systems that can provide recommendations based on additional contextual information besides the typical information on users and items used in most of the current recommender systems. This approach supports multiple dimensions, extensive profiling, and hierarchical aggregation of recommendations. The paper also presents a multidimensional rating estimation method capable of selecting two-dimensional segments of ratings pertinent to the recommendation context and applying standard collaborative filtering or other traditional two-dimensional rating estimation techniques to these segments. A comparison of the multidimensional and two-dimensional rating estimation approaches is made, and the tradeoffs between the two are studied. Moreover, the paper introduces a combined rating estimation method that identifies the situations where the MD approach outperforms the standard two-dimensional approach and uses the MD approach in those situations and the standard two-dimensional approach elsewhere. Finally, the paper presents a pilot empirical study of the combined approach, using a multidimensional movie recommender system that was developed for implementing this approach and testing its performance. 1 1.

This paper presents a probability of error based method of determining the saliency (usefulness) of input features and hidden nodes. We show that the partial derivative of the output nodes with respect to a given input feature yields a sensitivity measure for the probability of error. This partial derivative provides a saliency metric for determining the sensitivity of the feedforward network trained with a mean squared error learning procedure to a given input feature. 1 Introduction The multilayer perceptron has received a great deal of attention for implementation in pattern recognition tasks [5] [9] [15] [16] [17]. The results recently presented by Ruck [19] and Gish [4] have demystified the multilayer perceptron, showing that it approximates a Bayes optimal discriminant function [21][6]. This result allows us to find an understandable relationship between the output of the neural network and an input feature. The next section will illustrate the relationship between the probabili...

We present a novel algorithm for recovering a smooth manifold of unknown dimension and topology from a set of points known to belong to it. Numerous applications in computer vision can be naturally interpreted as instanciations of this fundamental problem. Recently, a non-iterative discrete approach, tensor voting, has been introduced to solve this problem and has been applied successfully to various applications.

Abstract: Personalization includes the adaptation of database queries according to the user’s needs, wishes and situation. We examine the influence of the dparameter as powerful personalization instrument for the Preference XPath search engine. Using a heuristic approach we present a possibility to deliver not only the qualitative best matching objects but also the desired amount of data to the user. Performing a series of test queries on proper e-catalog data, we demonstrate the effectiveness of our approach. 1

Automated design of superscalar processors can provide future system-on-chip (SOC) designers with a key-turn method of generating superscalar processors that are Pareto-optimal in terms of performance, energy consumption, and area for the target application program(s). Unfortunately, current optimization methods are based on time-consuming cycle-accurate simulation, unsuitable for analysis of hundreds of thousands of design options that is required to arrive at Pareto-optimal designs. This dissertation bridges the gap between a large design space of superscalar processors and the inability of cycle-accurate simulation to analyze a large design space, by providing a computationally and conceptually simple analytical method for generating Pareto-optimal superscalar processor designs. The proposed and evaluated analytical method consists of three parts: (1) a method for analytically estimating the performance in terms a cycles-per-instruction (CPI) using the application program statistics and the superscalar processor parameters, (2) a method of analytically estimating various energy consuming activities using the application program statistics and the superscalar processor parameters, and (3) a method of finding the Pareto-

This article uses canonical correlation analysis to investigate the income characteristics of active real estate agents in the United States who elected to participate in commercial and investment transactions. The model is unique in that it included activity areas to determine the specialties where agents generated the income and the type of clients who paid for the service. Future studies should consider the multiple dependent variable approach with activity areas to capture the relationship between income and the type of work involved.

Matthews coefficient probabilities: Improved estimates for unit cell contents of proteins, DNA, and protein–nucleic acid complex crystals

This research asked the following question: is there a correlation between types of organizational culture and factors influencing knowledge transfer? It hypothesized that organizations scoring high on the cultural factors of openness to change/innovation, and task-oriented organizational growth would tend to be fertile to knowledge transfer. Second, it hypothesized that organizations scoring high on the factors of bureaucratic and competition/confrontation would tend to be infertile to knowledge transfer.

Abstract: National Forests throughout the United States are facing critical management decisions regarding optimal resource use amidst strong countervailing pressures for access. Visitors to Talladega National Forest in Alabama were surveyed to develop appropriate marketing strategies. Cluster analysis showed that separate homogeneous user groups exist. This information was vital to the formation of appropriate marketing strategies. Forest based recreation is continually gaining participants. Opportunities to pursue traditional activities such as hunting and hiking, as well as new ventures such as rock climbing or rafting, may all occur within one National Forest area. The USDA Forest Service has a unique role: forest managers must consider demands for timber management as well as highly diverse forest recreational uses. The 217,000-acre Talladega National Forest (TNF)