There are various methods in knowledge space theory for building knowledge structures or surmise relations from data. Few of them have been thoroughly analyzed, making difficult to decide which of these methods provide good results and when to apply each of the methods. In this paper, we investigate the method inductive item tree analysis and discuss the advantages and disadvantages of this algorithm. In particular, we introduce some corrections and improvements to it, resulting in two newly proposed algorithms. These algorithms and the original inductive item tree analysis procedure are compared in a simulation study and with empirical data
. One of the defining challenges for the KDD research community is to enable inductive learning algo...
A crucial problem in knowledge space theory, a modern psychological test theory, is the derivation o...
The Web of Data, which is one of the dimensions of the Semantic Web (SW), represents a tremendous so...
There are various methods in knowledge space theory for building knowledge structures or surmise rel...
This work deals with data analysis methods in knowledge space theory (KST). In Chapter 2, the main d...
Item Tree Analysis (ITA) is an explorative method of data analysis which can be used to establish a ...
Abstract. Item Tree Analysis (ITA) can be used to mine determinis-tic relationships from noisy data....
This paper describes a method of explorative data analysis which allows to detect logical implicatio...
Knowledge space theory is part of psychometrics and provides a theoretical framework for the modelin...
Inductive reasoning entails using existing knowledge or observations to make predictions about novel...
Knowledge space theory is part of psychometrics and provides a theoretical framework for the modelin...
Abstract Inductive inferences about objects, features, catego-ries, and relations have been studied ...
Inductive learning enables the system to recognize patterns and regularities in previous knowledge o...
Induction methods have recently been found to be useful in a wide variety of business related proble...
<p>Inductive inferences about objects, features, categories, and relations have been studied for man...
. One of the defining challenges for the KDD research community is to enable inductive learning algo...
A crucial problem in knowledge space theory, a modern psychological test theory, is the derivation o...
The Web of Data, which is one of the dimensions of the Semantic Web (SW), represents a tremendous so...
There are various methods in knowledge space theory for building knowledge structures or surmise rel...
This work deals with data analysis methods in knowledge space theory (KST). In Chapter 2, the main d...
Item Tree Analysis (ITA) is an explorative method of data analysis which can be used to establish a ...
Abstract. Item Tree Analysis (ITA) can be used to mine determinis-tic relationships from noisy data....
This paper describes a method of explorative data analysis which allows to detect logical implicatio...
Knowledge space theory is part of psychometrics and provides a theoretical framework for the modelin...
Inductive reasoning entails using existing knowledge or observations to make predictions about novel...
Knowledge space theory is part of psychometrics and provides a theoretical framework for the modelin...
Abstract Inductive inferences about objects, features, catego-ries, and relations have been studied ...
Inductive learning enables the system to recognize patterns and regularities in previous knowledge o...
Induction methods have recently been found to be useful in a wide variety of business related proble...
<p>Inductive inferences about objects, features, categories, and relations have been studied for man...
. One of the defining challenges for the KDD research community is to enable inductive learning algo...
A crucial problem in knowledge space theory, a modern psychological test theory, is the derivation o...
The Web of Data, which is one of the dimensions of the Semantic Web (SW), represents a tremendous so...