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. / 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.
| Identifer | oai:union.ndltd.org:arizona.edu/oai:arizona.openrepository.com:10150/271493 |
| Date | 01 December 2012 |
| Creators | DharaniK, Kalpana Gudikandula |
| Contributors | 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 |
| Publisher | International Journal of Computer Science and Network (IJCSN) |
| Source Sets | University of Arizona |
| Language | English |
| Detected Language | English |
| Type | Technical Report |
| Relation | IJCSN-2012-1-6-16, 01, http://ijcsn.org/IJCSN-2012/1-6/IJCSN-2012-1-6-16.pdf |
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