Rayner Alfred

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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 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)
—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)
Abstrak This paper describes the performance of four crossover operators used in evolving the required controllers in a video game. The crossover operators used in this research are the two-point crossover, the uniform crossover, the N-point crossover, and the single-point crossover. The performance of these crossover methods were tested using Infinite(More)
—Due to the widespread use of relational databases (mySQL, Oracle, DB2, MsSQL), most data are stored as multiple tables in what can be a very large database. As a result, more efficient algorithms for mining data from multi-relational domain need to be implemented. Inductive Logic programming (ILP) techniques are useful for analyzing data in(More)
The ability to visualize documents into clusters is very essential. The best data summarization technique could be used to summarize data but a poor representation or visualization of it will be totally misleading. As proposed in many researches, clustering techniques are applied and the results are produced when documents are grouped in clusters. However,(More)
Abstrak The importance of selecting relevant features for data modeling has been recognized already in machine learning. This paper discusses the application of an evolutionary-based feature selection method in order to generate input data for unsupervised learning in DARA (Dynamic Aggregation of Relational Attributes). The feature selection process which(More)