Concept Relation Discovery and Innovation Enabling Technology (CORDIET), is a toolbox for gaining new knowledge from unstructured text data. At the core of CORDIET is the C-K theory which captures the essential elements of innovation. The tool uses Formal Concept Analysis (FCA), Emergent Self Organizing Maps (ESOM) and Hidden Markov Models (HMM) as main artifacts in the analysis process. The us...

With the increase in the size of datasets, data mining has become one of the most prevalent topics for research in database systems. The output from this process, the generation of rules of various types, has raised the question of how rules can be considered interesting. We argue that, in many cases, it is the meta-rule that holds the most interest. That is, given a set of known rules about a ...

Recently, there has been a lot of interest in temporal gran-ularity, and its applications in temporal dependency theory and data mining. Generalization hierarchies used in multi-dimensional databases and OLAP serve a role similar to that of time granularity in temporal databases, but they also apply to non-temporal dimensions, like space. In this paper, we rst generalize temporal functional dep...

Our planet is experiencing simultaneous changes in global population, urbanization, and climate. These changes, along with the rapid growth of climate data and increasing popularity of data mining techniques may lead to the conclusion that the time is ripe for data mining to spur major innovations in climate science. However, climate data bring forth unique challenges that are unfamiliar to the...

The hepatitis database contains the results of laboratory examinations taken on the patients of hepatitis B and C during 1982-2001, and recently was given to challenge data mining research. This paper presents our approach to two problems of distinguishing hepatitis B and C, and the relations between laboratory data and fibrosis stages. The approach is based on temporal abstraction and the visu...

Mining patterns of gene expression provides a crucial approach in discovering knowledge such as finding genetic networks that underpin the embryonic development. Analysis of mining results and evaluation of their relevance in the domain remains a major concern. In this paper we describe our explorative studies in support of solutions to facilitate the analysis and interpretation of mining resul...

Data mining used to find hidden knowledge from large amount of Databases. Periodic Pattern Mining is useful in Weather Forecasting, Fraud Detection and GIS Applications. In General, spatio-temporal pattern discovery process finds the partially ordered subsets of the eventtypes whose instances are located together and occur serially for a given collection of Boolean spatio-temporal event-types. ...

We study the problem of learning classification models from complex multivariate temporal data encountered in electronic health record systems. The challenge is to define a good set of features that are able to represent well the temporal aspect of the data. Our method relies on temporal abstractions and temporal pattern mining to extract the classification features. Temporal pattern mining usu...

A long-term research effort to support data mining applications for video databases of human motion is described. Due to the spatio-temporal nature of human motion data, novel methods for indexing and mining databases of time series data of human motion are required. Further, since data mining requires a significant sample size to accurately model patterns in the data, algorithms that automatic...