We present a general Multi-Agent System framework for distributed data mining based on a Peer-toPeer model. The framework adopts message-based asynchronous communication and a dyn...
Time series analysis is a wide area of knowledge that studies processes in their evolution. The classical research in the area tends to find global laws underlying the behaviour o...
Finding and removingoutliers is an important problem in data mining. Errors in large databases can be extremely common,so an important property of a data mining algorithm is robus...
System identification is an abductive task which is affected by several kinds of modeling assumptions and measurement errors. Therefore, instead of optimizing values of parameters ...
When mining large databases, the data extraction problem and the interface between the database and data mining algorithm become important issues. Rather than giving a mining algo...
Abstract. E cient data mining algorithms are crucial fore ective knowledge discovery. We present the Multi-Stream Dependency Detection (msdd) data mining algorithm that performs a ...
We present a new result concerning the parallelisation of DBSCAN, a Data Mining algorithm for density-based spatial clustering. The overall structure of DBSCAN has been mapped to a...
In this paper, a framework for replacing missing values in a database is proposed since a real-world database is seldom complete. Good data quality in a database can directly impr...
In today's industry, the design of software tests is mostly based on the testers' expertise, while test automation tools are limited to execution of pre-planned tests on...