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dc.contributor.authorHanees, A.L-
dc.contributor.authorDahana, H-
dc.identifier.citationAnnual Science Research Session 2014
dc.description.abstractData mining and knowledge discovery in Information system research have been attracting a significant amount of research domains. Information systems are powerful instruments for organizational problem solving through formal information processing. Data mining (DM) and knowledge discovery are intelligent tools that help to accumulate and process data and make use of it. Data mining bridges many technical areas, including databases, statistics, machine learning, and human-computer interaction. The set of data mining processes used to extract and verify patterns in data is the core of the knowledge discovery process. Numerous data mining techniques have recently been developed to extract knowledge from large databases. In this survey paper we consider some existing frameworks for data mining, including the reductionist statistical and probabilistic approaches, database perspective and inductive databases approach, constructive induction approach and data compression approach. This research presents methods and techniques together with their advantages and limitations analyzing what these approaches account in the data mining research and what they do not.en_US
dc.publisherFaculty of Applied science South Eastern University of Sri Lanka Oluvil # 32360 Sri Lankaen_US
dc.subjectInductive Databasesen_US
dc.subjectData Compressionen_US
dc.subjectMachine Learningen_US
dc.titleAnalysis on the use of information systems research methods in data miningen_US
dc.typeConference paperen_US
Appears in Collections:ASRS - FAS 2014

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