Geosensor technology is radically changing our ability to collect data in a wide range of domains. While sensor hardware and low-level system technology is advancing rapidly, higher level modeling needs to be advanced in parallel, so that users can effectively utilize the potential. This paper proposes an approach to modeling interaction with geosensor networks based on dynamic primitives. Much...

During recent years, the outstanding growth of social network communities has caught the attention of the research community. A huge amount of user-generated content is shared among community users and gives researchers the unique opportunity to thoroughly investigate social community behavior. Many studies have been focused on both developing models to investigate user and collective behavior ...

Knowledge discovery, that is, to analyze a given massive data set and derive or discover some knowledge from it, has been becoming a quite important subject in several fields including computer science. Good softwares have been demanded for various knowledge discovery tasks. For such softwares, we often need to develop efficient algorithms for handling huge data sets. Random sampling is one of ...

The goal of our current research is machine learning with the help and guidance of a knowledge base (KB). Rather than learning numerical models, our approach generates explicit symbolic hypotheses. These hypotheses are subject to the constraints of the KB and are easily human-readable and verifiable. Toward this end, we have implemented algorithms that hypothesize new relations and new types of...

Here we describe KODAMA (knowledge discovery by accuracy maximization), an unsupervised and semisupervised learning algorithm that performs feature extraction from noisy and high-dimensional data. Unlike other data mining methods, the peculiarity of KODAMA is that it is driven by an integrated procedure of cross-validation of the results. The discovery of a local manifold's topology is led by a...

Knowledge Discovery is traditionally used for analysis of large amounts of data and enables addressing a number of tasks that arise in Semantic Web and require scalable solutions. Additionally, Knowledge Discovery techniques have been successfully applied not only to structured data i.e. databases but also to semi-structured and unstructured data including text, graphs, images and video. Semant...

Knowledge discovery in databases or data mining is the semiautomated analysis of large volumes of data, looking for the relationships and knowledge that are implicit in large volumes of data and are ’interesting’ in the sense of impacting an organization’s practice. Data mining and knowledge discovery on large amounts of data can benefit of the use of parallel computers both to improve performa...

The framework as well as the particular algorithms of pattern recognition process is widely adopted in structural health monitoring (SHM). However, as a part of the overall process of knowledge discovery from data bases (KDD), the results of pattern recognition are only changes and patterns of changes of data features. In this paper, based on the similarity between KDD and SHM and considering t...

Actionable knowledge has been qualitatively and intensively studied in the social sciences. Its marriage with data mining is only a recent story. On the one hand, data mining has been booming for a while and has attracted an increasing variety of increasing applications. On the other, it is a reality that the so-called knowledge discovered from data by following the classic frameworks often can...

The main goal of this paper is to provide results and findings of some preliminary tests with two dif ferent clustering techniques suitable for knowledge discov ery in databases (KDD). We used Bayesian unsupervised classification (based on the AutoClass ystem) and the SelfOrganizing Map ( SOM) as a prominent unsupervised neural network to analyze a real-world atabase of ticket sales for castles...