In this paper, choosing highly frequent keywords from core journals in the field of 1992-2013 national knowledge discovery in CNKI database, counting the number of two frequent keywords co-occurrences in the same journal, then constructing the highly frequent keywords matrix, and transforming the highly frequent keywords matrix into a correlation matrix and a dissimilarity matrix, we analyze th...

Given its importance, the problem of object discovery in High-Resolution Remote-Sensing (HRRS) imagery has been given a lot of attention by image retrieval researchers. Despite the vast amount of expert endeavor spent on this problem, more effort has been expected to discover and utilize hidden semantics of images for image retrieval. To this end, in this paper, we exploit a hyperclique pattern...

How do instructors guide students to discover mathematical content? Are current explanatory models of pedagogical practice suitable to capture pragmatic essentials of discovery-based instruction? We examined videographed data from the implementation of a natural user interface design for proportions, so as to determine one constructivist tutor’s methodology for fostering expert visualization of...

We propose and evaluate an agendaand justificationbased architecture for discovery systems that contains a mechanism for selecting the next task to perform. This framework has many desirable properties: (1) its use of heuristics to perform and propose tasks facilitates the use of general discovery strategies that are able to use a variety of background knowledge, (2) through the use of justific...

This paper addresses issues and techniques for advanced age mortality study using data mining techniques, a new technology on the horizon with great actuarial potential. Data mining is an interactive information discovery process that includes data acquisition, data integration, data exploration, model building, and model validation. Both expert opinion and information discovery techniques are ...

The Knowledge Discovery in Databases and Data Mining field proposes the development of methods and techniques for assigning useful meanings for data stored in databases. It gathers researches from many study fields like machine learning, pattern recognition, databases, statistics, artificial intelligence, knowledge acquisition for expert systems, data visualization and grids. While Data Mining ...

The Knowledge Discovery in Databases and Data Mining field proposes the development of methods and techniques for assigning useful meanings for data stored in databases. It gathers researches from many study fields like machine learning, pattern recognition, databases, statistics, artificial intelligence, knowledge acquisition for expert systems, data visualization and grids. While Data Mining ...

Knowledge Discovery in Databases (KDD) is a complex and computationally intensive process, that requires a repeated interaction between tools and users, often in a distributed environment. Given the complexity of the process, both naïve and expert users need some support to effectively perform knowledge discovery. In this paper we present a userand knowledge-centric approach to support the desi...

In this paper, we propose a framework for iterative knowledge discovery from unstructured text using Formal Concept Analysis and Emergent Self Organizing Maps. We apply the framework to a real life case study using data from the Amsterdam-Amstelland police. The case zooms in on the problem of distilling concepts for domestic violence from the unstructured text in police reports. Our human-cente...

BACKGROUND The availability of large complex data sets generated by high throughput technologies has enabled the recent proliferation of disease biomarker studies. However, a recurring problem in deriving biological information from large data sets is how to best incorporate expert knowledge into the biomarker selection process. OBJECTIVE To develop a generalizable framework that can incorpor...