With the rapid proliferation of information technology, the increasing amount of information available has posted significant challenges on relevant information discovery for the users. An alternative way is to find an expert with specific expertise. Expert recommendation is important in variety of contexts ranging from industry to academia. Information retrieval methods or graph-based methods ...

Many data mining tools cannot be used directly to analyze the complex sets of relations which are found in large database systems. In our experience, data miners rely on a well-defined data model, or the knowledge of a data expert, to isolate and extract candidate data sets prior to mining the data. For many databases, typically large legacy systems, a reliable data model is often unavailable a...

This paper describes a formal representation of the discovery process that e ciently integrates of any number of data analysis strategies, regardless of their similarities and di erences. We have implemented a system based on this formalization, called the Scientist's Empirical Assistant (SEA). SEA employs several analysis strategies from the discovery literature, including techniques for funct...

We present a new approach to content determination and content organization in the context of natural language generation for quantitative database summaries. Three key properties make our work innovative and interesting: (1) we developed a new text planning approach to deals with the content organization of a data set into a summary report, for example a Data Mining discovery; (2) the approach...

In an intelligent tutoring system (ITS), the domain expert should provide relevant domain knowledge to the tutor so that it will be able to guide the learner during problem solving. However, in several domains, this knowledge is not predetermined and should be captured or learned from expert users as well as intermediate and novice users. Our hypothesis is that, knowledge discovery (KD) techniq...

This paper develops a knowledge discovery procedure for extracting knowledge of soil-landscape models from a soil map. It has broad relevance to knowledge discovery from other natural resource maps. The procedure consists of four major steps: data preparation, data preprocessing, pattern extraction, and knowledge consolidation. In order to recover true expert knowledge from the error-prone soil...

An empirical hierarchical Bayes approach was first proposed to analyze gene expression data collected from microarray experiments. Through a closed-form variance shrinkage estimator, information from multiple genes is pooled to increase the statistical power of multiple hypothesis testing. The approach also allows various types of subject matter knowledge to be incorporated conveniently. Caveat...

Abstract A rough neurocomputing approach to classifying power system faults is presented in this paper. Preprocessing fault data entails discretization of power system fault data obtained from the Transcan Recording System at Manitoba Hydro. An approach to discretizing power system fault data is briefly described in this article. After preprocessing, rough set methods are used to prepare fault ...

In this paper we discuss Conceptual Knowledge Discovery in Databases (CKDD) as it is developing in the eld of Conceptual Knowledge Processing (cf. 29],,30]). Conceptual Knowledge Processing is based on the mathematical theory of Formal Concept Analysis which has become a successful theory for data analysis during the last 18 years. This approach relies on the pragmatic philosophy of Ch.S. Peirc...

personalized medicine aims is to supply the proper drug to the proper patient within the right dose. pharmacogenomics (pgx) is to recognize genetic variants that may influence drug efficacy and toxicity. all things considered, the fields cover a wide area, including basic drug discovery researches, the genetic origin of pharmacokinetics and pharmacodynamics, novel drug improvement, patient gene...