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    Actionable Knowledge Discovery using Multi-Step Mining

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    Name:
    IJCSN-2012-1-6-16.pdf
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    Description:
    Actionable Knowledge Discovery ...
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    Author
    DharaniK
    Kalpana Gudikandula
    Affiliation
    Department of CS, JNTU H, DRK College of Engineering and Technology Hyderabad, Andhra Pradesh, India
    Department of IT, JNTU H, DRK Institute of Science and Technology Hyderabad, Andhra Pradesh, India
    Issue Date
    2012-12-01
    Keywords
    Data mining
    actionable knowledge discovery
    multimethod mining
    multi-feature mining
    
    Metadata
    Show full item record
    Publisher
    International Journal of Computer Science and Network (IJCSN)
    Journal
    International Journal of Computer Science and Network (IJCSN)
    Description
    Data 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.
    URI
    http://hdl.handle.net/10150/271493
    Additional Links
    http://ijcsn.org/IJCSN-2012/1-6/IJCSN-2012-1-6-16.pdf
    Abstract
    Data 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.
    Type
    Technical Report
    Language
    en
    Series/Report no.
    IJCSN-2012-1-6-16
    01
    ISSN
    2277-5420
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