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Problem statement: The importance of input representation has been recognized already in machine learning. Feature construction is one of the methods used to generate relevant features for learning data. This study addressed the question whether or not the descriptive accuracy of the DARA algorithm benefits from the feature construction process. In other(More)
The importance of input representation has been recognized already in machine learning. This article discusses the application of genetic-based feature construction methods to generate input data for the data summarization method called Dynamic Aggregation of Relational Attributes (DARA). Here, feature construction methods are applied to improve the(More)
Clustering is an essential data mining task with various types of applications. Traditional clustering algorithms are based on a vector space model representation. A relational database system often contains multi-relational information spread across multiple relations (tables). In order to cluster such data, one would require to restrict the analysis to a(More)
In this article, we report on our work on applying hierarchical agglomerative clustering (HAC) to a large corpus of documents where each appears both in Bulgarian and English. We cluster these documents for each language and compare the results both with respect to the shape of the tree and content of clusters produced. Clustering multilingual corpora(More)
Problem statement: In solving a classification problem in relational data mining, traditional methods, for example, the C4.5 and its variants, usually require data transformations from datasets stored in multiple tables into a single table. Unfortunately, we may loss some information when we join tables with a high degree of one-to-many association.(More)
This paper proposes tackling the difficult course timetabling problem using a multi-agent approach. The proposed design seeks to deal with the problem using a distributed solution environment in which a mediator agent coordinates various timetabling agents that cooperate to improve a common global solution. Initial timetables provided to the multi-agent(More)
In solving the classification problem in relational data mining, traditional methods, for example, the C4.5 and its variants, usually require data transformations from datasets stored in multiple tables into a single table. Unfortunately, we may loss some information when we join tables with a high degree of one-to-many association. Therefore, data(More)