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Identifier	000413398
Title	A rule-based data mining assistant
Alternative Title	Ένας καθοδηγητής εξόρυξης δεδομένων βασισμένης σε κανόνες
Author	Ξένου, Ρουμπίνη Π.
Select a value	Τσαμαρδινός, Ιωάννης
Reviewer	Πλεξουσάκης, Δημήτρης Στυλιανού, Ιωάννης
Abstract	Data mining, as well as Data Analysis and Machine Learning are utilized during the last few years, by a variety of people of different experience level and from different fields, to provide solutions in a variety of different domains and applications. Machine Learning algorithms are applied to any dataset, regardless of the type or content, and regardless of source e.g. real world or simulated, observations, and can generate a model describing them. The available data mining algorithm and methodologies space, is increased day by day, making it difficult even for experts to follow this changes. As different dataset types may demand, a different approach in terms of methodology or algorithmic analysis, the Data Analysis process is becoming even more complex. A lot of effort has been given towards the development of Data Mining Assistants (usually referred as IDAs - Intelligent Data Assistants), in order to overcome the above obstacles. In this Thesis we designed and developed an automated intelligent system, the RB-DMA (Rule Based Data Mining Assistant), which, based on an extension of the OntoDM data mining ontology [1] and combined with a set of rules written in Drools [2], proposes the most appropriate data mining workflows, ranked based on their efficiency for a given analysis. Our approach provides, all the decisions the end-user will need, regardless of their experience or knowledge, in order to conduct an analysis with trustful results. Data Analysis depending on the amount and complexity of data, usually require a considerable amount of time in order to produce results. Our system, addresses this issue, by proposing to the user the n best workflows, where n is considerably small and optimized to produce close to best results. Last, but not least, the system covers up to 200 data analysis scenarios (binary classification and regression, on a variety of data types and sizes).
Language	English
Subject	Automated assistant
	Αυτόματος βοηθός
Issue date	2017-11-24
Collection	School/Department--School of Sciences and Engineering--Department of Computer Science--Post-graduate theses
	Type of Work--Post-graduate theses
Permanent Link	https://elocus.lib.uoc.gr//dlib/6/b/8/metadata-dlib-1513758599-542110-8102.tkl
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