Sinh Hoa Nguyen

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We present a hierarchical scheme for synthesis of concept approximations based on given data and domain knowledge. We also propose a solution, founded on rough set theory, to the problem of constructing the approximation of higher level concepts by composing the approximation of lower level concepts. We examine the effectiveness of the layered learning(More)
ASSOCIATION RULE see extraction methods have been developed as the main methods for mining of real life data in particular in Basket Data Analysis In this paper we present a novel ap proach to generation of association rule based on Rough Set and Boolean reasoning methods We show the relationship between the problems of association rule extraction for(More)
We present an efficient method for decision tree construction from large data sets, which are assumed to be stored in database servers, and be accessible by SQL queries. The proposed method minimizes the number of simple queries necessary to search for the best splits (cut points) by employing “divide and conquer” search strategy. To make it possible, we(More)
We show that nding optimal discretization of instances of decision tables with two attributes with real values and binary decisions is computationally hard. This is done by abstracting the problem in such a way that it regards partitioning points in the plane into regions, subject to certain minimality restrictions, and proving them to be NP-hard. We also(More)
Sunspots are the subject of interest to many astronomers and solar physicists. Sunspot observation, analysis and classification form an important part of furthering the knowledge about the Sun. Sunspot classification is a manual and thus very labor intensive process that could be automated if it can be successfully learned by a machine. This paper deals(More)
Many learning methods ignore domain knowledge in synthesis of concept approximation. We propose to use hierarchical schemes for learning approximations of complex concepts from experimental data using inference diagrams based on domain knowledge. Our solution is based on the rough set and rough mereological approaches. The effectiveness of the proposed(More)
Searching for patterns is one of the main goals in data mining. Patterns have important applications in many KDD domains like rule extraction or classiication. In this paper we present some methods of rule extraction by generalizing the existing approaches for the pattern problem. These methods, called partition of attribute values or grouping of attribute(More)
This paper presents an application of hierarchical learning method based rough set theory to the problem of sunspot classification from satellite images. The Modified Zurich classification scheme [3] is defined by a set of rules containing many complicated and unprecise concepts, which cannot be determined directly from solar images. The idea is to(More)