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dc.contributor.authorDharaniK
dc.contributor.authorKalpana Gudikandula
dc.date.accessioned2013-03-07T23:34:48Z
dc.date.available2013-03-07T23:34:48Z
dc.date.issued2012-12-01
dc.identifier.issn2277-5420
dc.identifier.urihttp://hdl.handle.net/10150/271493
dc.descriptionData mining at enterprise level operates on huge amount of data such as government transactions, banks, insurance companies and so on. Inevitably, these businesses produce complex data that might be distributed in nature. When mining is made on such data with a single-step, it produces business intelligence as a particular aspect. However, this is not sufficient in enterprise where different aspects and standpoints are to be considered before taking business decisions. It is required that the enterprises perform mining based on multiple features, data sources and methods. This is known as combined mining. The combined mining can produce patterns that reflect all aspects of the enterprise. Thus the derived intelligence can be used to take business decisions that lead to profits. This kind of knowledge is known as actionable knowledge.en_US
dc.description.abstractData mining is a process of obtaining trends or patterns in historical data. Such trends form business intelligence that in turn leads to taking well informed decisions. However, data mining with a single technique does not yield actionable knowledge. This is because enterprises have huge databases and heterogeneous in nature. They also have complex data and mining such data needs multi-step mining instead of single step mining. When multiple approaches are involved, they provide business intelligence in all aspects. That kind of information can lead to actionable knowledge. Recently data mining has got tremendous usage in the real world. The drawback of existing approaches is that insufficient business intelligence in case of huge enterprises. This paper presents the combination of existing works and algorithms. We work on multiple data sources, multiple methods and multiple features. The combined patterns thus obtained from complex business data provide actionable knowledge. A prototype application has been built to test the efficiency of the proposed framework which combines multiple data sources, multiple methods and multiple features in mining process. The empirical results revealed that the proposed approach is effective and can be used in the real world.
dc.language.isoenen_US
dc.publisherInternational Journal of Computer Science and Network (IJCSN)en_US
dc.relation.ispartofseriesIJCSN-2012-1-6-16en_US
dc.relation.ispartofseries01en_US
dc.relation.urlhttp://ijcsn.org/IJCSN-2012/1-6/IJCSN-2012-1-6-16.pdfen_US
dc.subjectData miningen_US
dc.subjectactionable knowledge discoveryen_US
dc.subjectmultimethod miningen_US
dc.subjectmulti-feature miningen_US
dc.titleActionable Knowledge Discovery using Multi-Step Miningen_US
dc.typeTechnical Reporten_US
dc.contributor.departmentDepartment of CS, JNTU H, DRK College of Engineering and Technology Hyderabad, Andhra Pradesh, Indiaen_US
dc.contributor.departmentDepartment of IT, JNTU H, DRK Institute of Science and Technology Hyderabad, Andhra Pradesh, Indiaen_US
dc.identifier.journalInternational Journal of Computer Science and Network (IJCSN)en_US
refterms.dateFOA2018-05-18T09:53:20Z
html.description.abstractData mining is a process of obtaining trends or patterns in historical data. Such trends form business intelligence that in turn leads to taking well informed decisions. However, data mining with a single technique does not yield actionable knowledge. This is because enterprises have huge databases and heterogeneous in nature. They also have complex data and mining such data needs multi-step mining instead of single step mining. When multiple approaches are involved, they provide business intelligence in all aspects. That kind of information can lead to actionable knowledge. Recently data mining has got tremendous usage in the real world. The drawback of existing approaches is that insufficient business intelligence in case of huge enterprises. This paper presents the combination of existing works and algorithms. We work on multiple data sources, multiple methods and multiple features. The combined patterns thus obtained from complex business data provide actionable knowledge. A prototype application has been built to test the efficiency of the proposed framework which combines multiple data sources, multiple methods and multiple features in mining process. The empirical results revealed that the proposed approach is effective and can be used in the real world.


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